GREP in InDesign
Take advantage of support for GREP to power up Find/ Change and automate application of character styles, writes Sam Hampton-Smith.
GREP (global/regular expression/print) is a way to describe patterns in text. For example, if you want to find a word that starts with a 'D' and ends with a 'G', you can do that with GREP.
Particularly useful for long documents such as books, catalogues or newspapers, the power of GREP can be harnessed to deal with the removal of double spaces after full stops or to apply a style to every string of text inside a set of parenthesis.
GREP works by using a series of codes, known as expressions, to match strings of text, characters or numbers. By combining these codes into expressions, we can create very loose or very precise matches, far beyond the usual 'Find and Replace' functionality. Even better, with InDesign CS4 's support for GREP styles we can set up rules that will automatically style match text, even if it's added after you've formatted the document. In this tutorial we're going to set up some automatic formatting for a food and recipe magazine.
Click here to download the support files (3.87MB)
Click here to download the tutorial for free
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Top 60 Print Advertising Agencies in Raleigh
- Print Advertising
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Looking for a top print ad agency in Raleigh?
Print advertisements appear in a printed or hard copy format. For example, you may come across print ads in newspapers, magazines, or brochures. Read more + The best print advertising has a clear message, is visual, and keeps everything simple. Some benefits of print advertising include cost effectiveness compared to large-scale out of home (OOH) advertisements; flexibility when it comes to having multiple print ad campaigns throughout the year; and reputation-building since these ads appear in the newspapers and magazines people browse regularly.
To assist you in your search for a partner, we’ve compiled this list of the top print advertising companies in Raleigh. Browse descriptions, feedback, and awards to find which can best suit your company’s needs.
List of the Best Raleigh Print Advertising Companies
- Min. Project Size "> $25,000+
- Company Size "> 10 - 49 employees
- Location "> Raleigh, NC
THE REPUBLIK is a creative agency headquartered in Raleigh, North Carolina. Founded in 2001, their team of approximately 23 employees primarily serves small to midsized businesses. Their scope of services includes branding, marketing strategy, social media management, and more.
- Hyundai Living & Culture USA
- NC Convention and Visitor's Bureau
- Dilworth Coffee
- Advertising & marketing " >
THE REPUBLIK is leading a rebrand for a major shopping center. They handled a sensitive remaining process, updated the client’s brand materials, designed murals, and are now planning media production.
"They balance creativity and strategy in a way I’ve never seen from an agency."
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The Marketing Machine
- Min. Project Size "> $1,000+
- Company Size "> 2 - 9 employees
The Marketing Machine is a marketing and branding company based in Raleigh, N.C. This company was founded in 1998 and has over 10 employees. A creative services agency aimed at small and medium-sized businesses, The Marketing Machine specializes in marketing expertise, branding strategy, campaign planning, and graphic design solutions.
- Garner Appliance and Mattress
- Mandel Communications
- Business services " >
- Consumer products & services " >
- Medical " >
- Hospitality & leisure " >
The Marketing Machine elevated and insurance company’s brand by redesigning their business cards and hosting a photo shoot for the website.
"The Marketing Machine has, in many ways, become part of my consulting team. I trust their professionalism and advice."
- Min. Project Size "> $5,000+
- Location "> Chapel Hill, NC
Rivers Agency is a design and digital agency founded in 1993 and located in Chapel Hill, N.C. The team of about 50 employees offers advertising, branding, digital strategy, web development, and e-commerce development services. Their clientele includes enterprises and midmarket businesses in the business services, education, financial services, and IT industries.
- Fantastic Sams
- Hill Learning Center
- Carolina Performing Arts
- Arts, entertainment & music " >
- Education " >
- Financial services " >
- Information technology " >
Rivers Agency redesigned a candy company’s WordPress website and switched their e-commerce site to Shopify. They used preexisting brand guidelines for their design work.
"I thought that they were excellent."
Worked with REVERED Agency?
- Min. Project Size "> Undisclosed
Worked with Clean Design?
- Min. Project Size "> $10,000+
- Company Size "> 50 - 249 employees
- Telecommunications " >
- Supply Chain, Logistics, and Transport " >
Worked with Publicus Community?
Good Soup Creative
Worked with Good Soup Creative?
- Location "> Cary, NC
Media Partners, Inc. (MPI)
Worked with Media Partners, Inc. (MPI)?
BtB Marketing Communications
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- Location "> Apex, NC
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- Location "> Wake Forest, NC
Worked with Baldwin&?
Advertising company Baldwin& was launched in 2009. The company focuses on advertising, branding, digital strategy, social media marketing, and more and has a small team. The company is headquartered in Raleigh, North Carolina.
Worked with vitalink?
All Inclusive Management Group
Worked with All Inclusive Management Group?
Indigo Graphic Design
Worked with Indigo Graphic Design?
- Company Size "> Freelancer employees
- Manufacturing " >
- Energy & natural resources " >
- Non-profit " >
Worked with Anoroc Agency?
- Real estate " >
Worked with McKinney?
- Location "> New York, NY
Worked with Adcart?
- Location "> Durham, NC
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Leach Advisors LLC
Worked with Leach Advisors LLC?
Leach Advisors LLC is a small branding company. They provide branding, public relations, logo, advertising, and more and were founded in 2018.
Brand It Inc
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Worked with Engine Brandmakers?
Worked with queue?
Queue, an advertising company, was established in 2012. Their small team is based in Raleigh, North Carolina and offers advertising, branding, digital strategy, social media marketing, and more.
North Star Marketing
Worked with North Star Marketing?
- Location "> Burlington, NC
Partin Design Group
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- Location "> Graham, NC
Worked with Honestly?
- Location "> Greensboro, NC
- eCommerce " >
Worked with Tangram Media?
- Location "> Pinehurst, NC
Worked with RLF Communications?
Triad Search Marketing
Worked with Triad Search Marketing?
Worked with Emisare, Inc.?
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- APA vs. MLA | The Key Differences in Format & Citation
APA vs MLA | The Key Differences in Format & Citation
Published on January 9, 2020 by Shona McCombes . Revised on August 23, 2022.
APA Style Is defined in the Publication Manual of the American Psychological Association , currently in its 7th edition .
The rules of MLA style are found in the MLA Handbook , currently in its 9th edition (published by the Modern Language Association).
In both styles, a source citation consists of:
- A brief parenthetical citation in the text
- A full reference at the end of the paper
However, citations look slightly different in each style, with different rules for things like title capitalization, author names, and placement of the date.
There are also some differences in layout and formatting . Download the Word templates for a correctly formatted paper in either style.
APA template MLA template
Table of contents
Which style should i use, in-text citations in apa and mla, apa reference list vs. mla works cited list, apa vs. mla paper formatting, frequently asked questions about citation styles.
You’ll usually be told which citation style you should use in your writing by your department or supervisor. If you’re not sure, look up your institution’s guidelines or ask directly.
Occasionally, you may be allowed to choose a style yourself. If so, it’s best to base your decision on your area of study:
- APA is used primarily in the (social and behavioral) sciences and in fields related to education.
- MLA is primarily used in humanities subjects such as languages, literary studies, and media studies.
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Both MLA and APA use parenthetical citations to cite sources in the text. However, they include slightly different information.
An APA in-text citation includes the author’s last name and the publication year. If you’re quoting or paraphrasing a specific passage, you also add a page number.
An MLA in-text citation includes the author’s last name and a page number—no year.
When there are two authors, APA Style separates their names with an ampersand (&), while MLA uses “and.” For three or more authors, both styles list the first author followed by “ et al. ”
In both APA and MLA style, you list full details of all cited sources on a separate page at the end of your paper. In APA this is usually called the reference list ; in MLA it’s called the Works Cited .
The formatting of source entries is different in each style. Some key differences are summarized in the table below.
Using the interactive tool, you can switch between APA and MLA style citations for common source types to explore the differences for yourself.
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The general formatting guidelines for APA and MLA are similar. Both styles recommend:
- 12 pt Times New Roman font
- Double spacing
- 1 inch (2.54 cm) margins
The main differences between APA format and MLA format involve the title page, running head, and block quoting guidelines.
Title page and header
In APA, a separate cover page is required. It lists the title of your paper, your full name, your institution and department, the course the paper is for, your instructor’s name, and the due date, all centered and double-spaced.
In MLA, no title page is required (though your instructor may require you to include one ). Instead of a title page, you add a four-line header on the first page.
The header is left-aligned and double-spaced and lists your full name, your instructor’s name, the course title or number, and the submission date. The paper’s title is centered on a new line under the header.
In APA Style, include a right-aligned page number at the top of each page.
In manuscripts that will be submitted for publication, you should also include an APA running head with a shortened version of your paper’s title (up to 50 characters long), all in capitals and left-aligned.
The running head is not required in student papers (unless you’re instructed otherwise).
Block quote formatting
Block quotes are long quotations that are set on a new line and indented as a block, without quotation marks.
In APA, any quote of 40 words or longer should be formatted as a block quote. In MLA, block quote formatting is used for quotes of more than four lines of prose or more than three lines of verse.
In both styles, the in-text citation is added after the period at the end of a block quote.
- APA block quote example
- MLA block quote example
The reader quickly becomes familiar with Nick Carraway’s relationship with Jay Gatsby, as the very first mention of the character illustrates both his admiration and disdain:
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APA and MLA style both use parenthetical in-text citations to cite sources and include a full list of references at the end, but they differ in other ways:
- APA in-text citations include the author name, date, and page number (Taylor, 2018, p. 23), while MLA in-text citations include only the author name and page number (Taylor 23).
- The APA reference list is titled “References,” while MLA’s version is called “ Works Cited .”
- The reference entries differ in terms of formatting and order of information.
- APA requires a title page , while MLA requires a header instead.
Check if your university or course guidelines specify which citation style to use. If the choice is left up to you, consider which style is most commonly used in your field.
- APA Style is the most popular citation style, widely used in the social and behavioral sciences.
- MLA style is the second most popular, used mainly in the humanities.
- Chicago notes and bibliography style is also popular in the humanities, especially history.
- Chicago author-date style tends to be used in the sciences.
Other more specialized styles exist for certain fields, such as Bluebook and OSCOLA for law.
The most important thing is to choose one style and use it consistently throughout your text.
APA format is widely used by professionals, researchers, and students in the social and behavioral sciences, including fields like education, psychology, and business.
Be sure to check the guidelines of your university or the journal you want to be published in to double-check which style you should be using.
MLA Style is the second most used citation style (after APA ). It is mainly used by students and researchers in humanities fields such as literature, languages, and philosophy.
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McCombes, S. (2022, August 23). APA vs MLA | The Key Differences in Format & Citation. Scribbr. Retrieved September 27, 2023, from https://www.scribbr.com/citing-sources/apa-vs-mla/
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Human population growth and the demographic transition
The world and most regions and countries are experiencing unprecedentedly rapid demographic change. The most obvious example of this change is the huge expansion of human numbers: four billion have been added since 1950. Projections for the next half century expect a highly divergent world, with stagnation or potential decline in parts of the developed world and continued rapid growth in the least developed regions. Other demographic processes are also undergoing extraordinary change: women's fertility has dropped rapidly and life expectancy has risen to new highs. Past trends in fertility and mortality have led to very young populations in high fertility countries in the developing world and to increasingly older populations in the developed world. Contemporary societies are now at very different stages of their demographic transitions. This paper summarizes key trends in population size, fertility and mortality, and age structures during these transitions. The focus is on the century from 1950 to 2050, which covers the period of most rapid global demographic transformation.
After centuries of very slow and uneven growth, the world population reached one billion in 1800. The modern expansion of human numbers started then, rising at a slow but more steady pace over the next 150 years to 2.5 billion in 1950. During the second half of the twentieth century, however, growth rates accelerated to historically unprecedented levels. As a result, world population more than doubled to 6.5 billion in 2005 (United Nations 1962 , 1973 , 2007 ). This population expansion is expected to continue for several more decades before peaking near 10 billion later in the twenty-first century. Around 2070, the world's population will be 10 times larger than in 1800.
The recent period of very rapid demographic change in most countries around the world is characteristic of the central phases of a secular process called the demographic transition . Over the course of this transition, declines in birth rates followed by declines in death rates bring about an era of rapid population growth. This transition usually accompanies the development process that transforms an agricultural society into an industrial one. Before the transition's onset, population growth (which equals the difference between the birth and death rate in the absence of migration) is near zero as high death rates more or less offset the high birth rates typical of agrarian societies before the industrial revolution. Population growth is again near zero after the completion of the transition as birth and death rates both reach low levels in the most developed societies. During the intervening transition period, rapid demographic change occurs, characterized by two distinct phases. During the first phase, the population growth rate rises as the death rate declines while the birth rate remains high. In the second phase, the growth rate declines (but remains positive) due to a decline in the birth rate. The entire transition typically takes more than a century to complete and ends with a much larger population size.
The plot of world population size over time in figure 1 (top solid line) shows the typical S-shaped pattern of estimated and projected population size over the course of the transition. Population growth accelerated for most of the twentieth century reaching the transition's midpoint in the 1980s and has recently begun to decelerate slightly. Today, we are still on the steepest part of this growth curve with additions to world population exceeding 75 million per year between 1971 and 2016.
Population size estimates, 1900–2005 and projections 2005–2050. High, medium and low variants.
Contemporary societies are at very different stages of their demographic transitions. Key trends in population size, fertility and mortality during these transitions are summarized below. The focus is on the century from 1950 to 2050, covering the period of most rapid global demographic change. The main source of data is the United Nation's 2006 world population assessment, which provides estimates for 1950–2005 and projections from 2005 to 2050 ( United Nations 2007 ).
2. Future population trends
The projected rise in world population to 9.2 billion in 2050 represents an increase of 2.7 billion over the 2005 population of 6.5 billion. Nearly all of this future growth will occur in the ‘South’—i.e. Africa, Asia (excluding Japan, Australia and New Zealand), and Latin America—where population size is projected to increase from 5.3 to 7.9 billion between 2005 and 2050 ( table 1 ). In contrast, in the ‘North’ (Europe, Northern America, Japan and Australia/New Zealand), population size is forecast to remain virtually stable, growing slightly from 1.22 to 1.25 billion between 2005 and 2050. The difference in trends between these two world regions reflects the later stage of the transition in the North compared with the South.
Population estimates (1950–2005) and projections (2005–2050), by region. Adapted from United Nations (2007) .
The global demographic transition began in the nineteenth century in the now economically developed parts of the world (the North) with declines in death rates. Large reductions in birth rates followed in the early part of the twentieth century. These transitions are now more or less complete. But, as shown in table 1 , trends for the two principal regions in the North are expected to diverge between 2005 and 2050: an increase from 0.33 to 0.45 billion in Northern America, and a decline from 0.73 to 0.66 billion in Europe. In fact, several countries in Europe (e.g. Russia) and East Asia (e.g. Japan) face significant population declines as birth rates have fallen below death rates.
The demographic transitions in Africa, Asia and Latin America started later and are still underway. In 2005, Asia had a population of 3.94 billion, more than half of the world total, and its population is expected to grow by 34 per cent to 5.27 billion by 2050. Africa, with 0.92 billion inhabitants in 2005, is likely to experience by far the most rapid relative expansion, more than doubling to 2.0 billion by 2050. Latin America, with 0.56 billion in 2005, is the smallest of the regions of the South; its projected growth trend is similar to that of Asia.
It may seem surprising that population growth continues at a rapid pace in sub-Saharan Africa, where the AIDS epidemic is most severe. This epidemic has indeed caused many deaths, but population growth continues because the epidemic is no longer expanding and the birth rate is expected to remain higher than the elevated death rate in the future ( UNAIDS 2007 ; Bongaarts et al . 2008 ). The epidemic's demographic impact can be assessed by comparing the standard UN population projection (which includes the epidemic's effect) with a separate hypothetical projection in which AIDS mortality is excluded ( United Nations 2007 ). In sub-Saharan Africa, the former projects a 2050 population of 1.76 billion and the latter a population of 1.95 billion. The difference of 0.2 billion in 2050 between these projections with and without the epidemic is due to deaths from AIDS as well as the absence of the descendents from people who died from AIDS. According to these projections, the population of sub-Saharan Africa will grow by one billion between 2005 and 2050 despite the substantial impact of the AIDS epidemic. In fact, no country is expected to see a decline in its population size between 2005 and 2050 due to high AIDS mortality. Most populations in sub-Saharan Africa will more than double in size, several will triple and Niger is expected to quadruple by 2050 ( United Nations 2007 ).
Transitions in the developing world have generally produced more rapid population growth rates in mid-transition than historically observed in the North. In some developing countries (e.g. Kenya and Uganda), peak growth rates approached four per cent per year in recent decades (implying a doubling of population size in two decades), levels that were very rarely observed in developed countries except with massive immigration. Two factors account for this very rapid expansion of population in these still largely traditional societies: the spread of medical technology (e.g. immunization, antibiotics) after World War II, which led to extremely rapid declines in death rates, and a lag in declines in birth rates.
Population sizes for the 10 largest countries in 2005 and in 2050 are presented in table 2 . In 2005, China (1.31 billion) and India (1.13 billion) were by far the largest countries, together accounting for nearly half the South's total. The top 10 include six Asian countries and only one country each in Latin America and Africa. By 2050, the ranking is expected to have shifted substantially, with India's population exceeding China's, and with Ethiopia and DR Congo rising to the top 10, replacing Japan and the Russian Federation.
Ten largest countries by population size in 1995 (estimate) and 2050 (medium projection). Adapted from United Nations (2007) .
To simplify the presentation of results, all projections discussed in this study are taken from the medium variant of the UN projections ( United Nations 2007 ). The UN has a good record of making relatively accurate projections ( National Research Council 2000 ), but the future is of course uncertain and actual population trends over the next half century will likely diverge to some extent from current projections. The UN makes an effort to capture this uncertainty by publishing separate high and low projections. For the world, the high and low variants reach 7.8 and 10.8 billion, respectively, in 2050, indicating a rather wide range of possible outcomes (see dashed lines in figure 1 ).
3. Drivers of population growth: fertility and mortality
The world's population increases every year because the global birth rate exceeds the death rate. For example, in 2000–2005 population size increased at a rate of 1.17 per cent per year, which equals the difference between a birth rate of 2.03 per cent and a death rate of 0.86 per cent. At the country level, population growth is also affected by migration, but for the regional aggregates of population used in this analysis, migration is usually a minor factor, and it will therefore not be discussed in detail.
The annual birth and death rates of populations are in turn primarily determined by levels of fertility and mortality experienced by individuals. The most widely used fertility indicator is the total fertility rate (TFR), which equals the number of births a woman would have by the end of her reproductive years if she experienced the age-specific fertility rates prevailing in a given year. Mortality is often measured by the life expectancy (LE) at birth, which equals the average number of years a newborn would live if subjected to age-specific mortality rates observed in a given year.
The UN's past estimates and future projections of fertility levels by region for the period 1950–2050 are presented in figure 2 . In the 1950s, the TFR in the South was high and virtually stable at around six births per woman on average. This high level of fertility reflects a near absence of birth control, a condition that has prevailed for centuries before the middle of the twentieth century. In the late 1960s, a rapid decline in fertility started nearly simultaneously in Asia and Latin America. In contrast, Africa has experienced only limited reproductive change. As a result of these divergent past trends, fertility levels in 2000–2005 differed widely among regions from as high as 5 births per woman (bpw) in Africa, to 2.5 bpw in Asia and Latin America. Average fertility in the North was already low in the early 1950s and has since declined to 2.0 bpw in Northern America and to 1.4 bpw in Europe.
Trends in the total fertility rate by region.
The decline in the average fertility in the South from 6 to 3 bpw over the past half century has been very rapid by historical standards. This reproductive revolution is mainly due to two factors. First, desired family size of parents has declined as the cost of children rose and child survival increased. Second, government intervention played a key role. In China this took the form of a coercive and unpopular one-child policy, but most other countries implemented voluntary family planning programmes. The aim of these programmes is to provide information about and access to contraceptives at subsidized prices so that women who want to limit their childbearing can more readily do so.
UN projections for the South assume that the TFR will eventually reach and then fall slightly below the so-called ‘replacement’ level in all regions. Replacement fertility is just above 2 bpw and it represents the level at which each generation just replaces the previous one, thus leading to zero population growth (in the absence of mortality change and migration). Below-replacement fertility produces, in the long run, population decline. As is evident from figure 2 , the TFRs in Asia and Latin America are expected to reach the replacement level around 2020. Africa is assumed to be on a much slower trajectory towards replacement fertility because of its lower level of socio-economic development. High fertility therefore remains a key cause of future population growth in this region. In contrast, the already low fertility of the North is expected to remain below replacement and is no longer driving population growth.
(b) Mortality and life expectancy
Mortality levels have also changed rapidly over the past several decades ( figure 3 ). The South experienced exceptional improvements in LE from an average of 41 years in 1950–1955 to 64 years in 2000–2005. By the early 2000, Latin America reached mortality levels similar to those prevailing in the North in the 1970s, and Asia was just a few years behind. Africa experienced the highest mortality and improvements in LE stalled in the 1990s due to the AIDS epidemic. As a result, Africa's LE, at 52 years in 2000–2005, was still substantially below that of Asia (68) and Latin America (72). As expected, Europe and Northern America already achieved relatively low levels of mortality by 1950, but they have nevertheless seen significant further improvements since then. Europe's LE (74) is now lower than North America's (78) because of a rise in mortality in Eastern Europe after the break-up of the Soviet Union.
Trends in LE by region.
Projections of future LEs by the UN assume continued improvements over time in all regions. The North is expected to reach 82 years in 2050 despite the increasing difficulty in achieving increments as countries reach ever higher levels of LE. Asia and Latin America are expected to continue to close the gap with the North, and Africa will continue to lag, in part because the continent remains affected by the AIDS epidemic.
It should be noted that the assumptions made by the UN about future trends in fertility and mortality are not based on a firm theoretical basis. Instead, the UN relies on empirical regularities in past trends in countries that have completed their transitions, mostly in the North, where fertility declined to approximately the replacement level, and increases in LE became smaller over time. This is a plausible approach that unfortunately leaves room for potential inaccuracies in projection results.
4. Changing population age composition
Over the course of the demographic transition, declines in fertility and mortality cause important changes in a population's age composition. In general, countries in the early stages of the transition have a younger age structure than countries in the later stages.
Figure 4 presents the distribution of the 2005 population in four broad age groups: 0–14, 15–24, 25–64 and 65+ by region. Most of the regions in the South—Africa, Latin America, South Asia and West Asia—have very young age structures with about half of the population under age 25 (62% in Africa). The exception is East Asia (mostly China) where this proportion is 37 per cent. In the North, the population under 25 is still smaller: 35 per cent in North America and just 30 per cent in Europe. The reverse pattern is observed for the proportion 65+, which is much higher in the North than in the South, ranging from as high as 15 per cent in Europe to as low as just 3 per cent in Africa.
Distribution of population by age, by region, 2005.
(a) The age-dependency ratio
A changing age distribution has significant social and economic consequences, e.g. for the allocation of education, healthcare and social security resources to the young and old. Assessments of this impact often rely on the so-called age-dependency ratio (DR) that summarizes key changes in the age structure. The DR at a given point in time equals the ratio of population aged below 15 and over 65 to the population of age 15–64. This ratio aims to measure how many ‘dependents’ there are for each person in the ‘productive’ age group. Obviously, not every person below 15 and over 65 is a dependent and not every person between ages 15 and 65 is productive. Despite its crudeness, this indicator is widely used to document broad trends in the age composition.
Over the course of a demographic transition, the DR shows a characteristic pattern of change. Figure 5 presents this pattern as observed in the South from 1950 to 2005 and projected from 2005 to 2050. Early in the transition, the DR typically first rises slightly as improvements in survival chances of children raise the number of young people. Next, the DR falls sharply as declines in fertility reduce the proportion of the population under age 15. This decline has important economic consequences because it creates a so-called ‘demographic dividend’, which boosts economic growth by increasing the size of the labour force relative to dependents and by stimulating savings ( Birdsall et al . 2001 ). Finally, at the end of the transition, the DR increases again as the proportion of the population over age 65 rises. Figure 5 also plots the DR of the North from 1950–2050. From 1950 to 2010 it showed a slight decline, but after 2010 it rises steeply as very low fertility and increasing longevity increases the proportion 65+. This ageing of the North poses serious challenges to support systems for the elderly (OECD 1998 , 2001 ).
Dependency ratio estimates, 1950–2005.
(b) Population momentum
At the end of the demographic transition natural population growth reaches zero once three conditions are met:
- Fertility levels-off at the replacement level of about 2.1 bpw (more precisely, the net reproduction rate should be 1). If fertility remains above replacement, population growth continues.
- Mortality stops declining. In practice, this is not likely to happen because improvements in medical technology and healthcare as well as changes in lifestyles, etc. will probably ensure continued increases in LE.
- The age structure has adjusted to the post-transitional levels of fertility and mortality.
The adjustment of the age structure at the end of the transition takes many decades to complete. A key implication of this slow adjustment process is that population growth continues for many years after replacement fertility is reached if, as is often the case, the population is still relatively young when fertility reaches the replacement level. The tendency of population size to increase after a two-child family size has been reached is referred to as population momentum ; it is the consequence of a young population age structure (‘young’ is defined relative to the age structure in the current life table) ( Bongaarts & Bulatao 1999 ).
The population momentum inherent in the age structure of a particular population at a given point in time can be estimated with a hypothetical population projection in which future fertility is set instantly to the replacement level, mortality is held constant and migration is set to zero. Since such a variant is not directly available from UN projections, it will not be presented here. However, the UN does provide ‘instant replacement’ projections in which mortality and migration trends are the same as in the standard projection. This projection gives an approximation of the combined effect on future growth of population momentum and declining mortality in the South because the role of migration is small. The difference between this hypothetical projection and the standard medium UN projection is a measure of the impact of high fertility on future population growth.
Results of these two projections are presented in figure 6 , which compares the per cent growth between 2005 and 2050 for regions in the South. The black bars give the growth in the standard (medium variant) projection and the grey bars give the growth in the ‘instant replacement’ projection. Three results are noteworthy. First, the two projections differ most in Africa (+117% versus +50%) which is as expected because fertility is still very high in this region. Second, in all regions of the South outside China, populations would be expected to rise by 50 per cent (62% in West Asia) if fertility were set to replacement in 2005. This implies that momentum and declining mortality are responsible for nearly half of the projected future population growth in Africa and for the large majority of growth in Latin America, and South and West Asia. Third, in East Asia and in Latin America the replacement projection exceeds the medium UN projection. This finding is explained by the fact that fertility in these regions is assumed to average below the replacement level over the next half century.
Percentage increase in population 2005–2050, by region, alternative projections. Black bars denote medium UN projection; grey bars denote instant replacement projection (hypothetical).
The world and most countries are going through a period of unprecedentedly rapid demographic change. The most obvious example of this change is the huge expansion of human numbers: four billion have been added since 1950. Other demographic processes are also experiencing extraordinary change: women are having fewer births and LEs have risen to new highs. Past trends in fertility and/or mortality have led to very young populations in high fertility countries in the South and to increasingly older populations in the North. Still other important demographic changes which were not reviewed here include rapid urbanization, international migration, and changes in family and household structure.
Global population growth will continue for decades, reaching around 9.2 billion in 2050 and peaking still higher later in the century. The demographic drivers of this growth are high fertility in parts of the South, as well as declining mortality and momentum. This large expansion in human numbers and of the accompanying changes in the age structure will have multiple consequences for society, the economy and the environment as discussed in the subsequent chapters in this issue.
One contribution of 14 to a Theme Issue ‘ The impact of population growth on tomorrow's world ’.
- Birdsall N., Kelley A., Sinding S.2001 Population matters: demographic change, economic growth and poverty in the developing world Oxford, UK: Oxford University Press [ Google Scholar ]
- Bongaarts J., Bulatao R.1999 Completing the demographic transition . Popul. Dev. Rev. 25 , 515–529 ( doi:10.1111/j.1728-4457.1999.00515.x ) [ Google Scholar ]
- Bongaarts J., Buettner J., Heilig G., Pelletier F.2008 Has the AIDS epidemic peaked? Popul. Dev. Rev. 34 , 199–224 ( doi:10.1111/j.1728-4457.2008.00217.x ) [ Google Scholar ]
- National Research Council 2000 Beyond six billion: forecasting the world's population (eds Bongaarts J., Bulatao R.). Washington, DC: National Academy Press [ Google Scholar ]
- OECD 1998 Maintaining prosperity in an ageing society Paris: OECD Publications [ Google Scholar ]
- OECD 2001 The fiscal implications of ageing: projections of age-related spending . OECD Economic Outlook 69 , 145–167 [ Google Scholar ]
- UNAIDS 2007 AIDS Epidemic Update Geneva: UNAIDS [ Google Scholar ]
- United Nations 1962 Demographic yearbook New York, NY: United Nations [ Google Scholar ]
- United Nations 1973 The determinants and consequences of population trends New York, NY: Department of Economic and Social Affairs, Population Studies 50, United Nations [ Google Scholar ]
- United Nations 2007 World population prospects: the 2006 revision New York, NY: United Nations Population Division [ Google Scholar ]
- Open Access
- Published: 21 September 2021
Local government responses for COVID-19 management in the Philippines
- Dylan Antonio S. Talabis 1 , 2 ,
- Ariel L. Babierra 1 , 2 ,
- Christian Alvin H. Buhat 1 , 2 ,
- Destiny S. Lutero 1 , 2 ,
- Kemuel M. Quindala III 1 , 2 &
- Jomar F. Rabajante 1 , 2 , 3
BMC Public Health volume 21 , Article number: 1711 ( 2021 ) Cite this article
Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)–municipalities and provinces–can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods.
Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y ) were compared to the economic, health, and demographic data (dataset X ) using Euclidean metric d =( x − y ) 2 , where x ∈ X and y ∈ Y . If a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be a ‘good’ outlier.
Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the ‘good’ outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a ‘good’ outlier with respect to demographic factors.
Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country’s preparedness for future health emergencies.
Peer Review reports
Since the emergence of the COVID-19 pandemic, the number of cases have already reached 82 million worldwide at the end of 2020. In the Philippines, the number of cases exceeded 473,000. As countries around the world face the continuing threat of the COVID-19 pandemic, national governments and health ministries formulate, implement and revise health policies and standards based on recommendations by world health organization (WHO), experiences of other countries, and on-the-ground experiences. Early health measures were primarily aimed at preventing and reducing transmission in populations at risk. These measures differ in scale and speed among countries, as some countries have more resources and are more prepared in terms of healthcare capacity and availability of stringent policies [ 1 , 2 ].
During the first months of the pandemic, several countries struggled to find tolerable, if not the most effective, measures to ‘flatten’ the COVID-19 epidemic curve so that health facilities will not be overwhelmed [ 3 , 4 ]. In responding to the threat of the pandemic, public health policies included epidemiological and socio-economic factors. The success or failure of these policies exposed the strengths or weaknesses of governments as well as the range of inequalities in the society [ 5 , 6 ].
As national governments implemented large-scale ‘blanket’ policies to control the pandemic, local government units (LGUs) have to consider granular policies as well as real-time interventions to address differences in the local COVID-19 transmission dynamics due to heterogeneity and diversity in communities. Some policies in place, such as voluntary physical distancing, wearing of face masks and face shields, mass testing, and school closures, could be effective in one locality but not in another [ 7 – 9 ]. Subnational governments like LGUs are confronted with a health crisis that have economic, social and fiscal impact. While urban areas have been hot spots of the COVID-19 pandemic, there are health facilities that are already well in placed as compared to less developed and deprived rural communities [ 10 ]. The importance of local narratives in addressing subnational concerns are apparent from published experiences in the United States [ 11 ], China [ 12 , 13 ], and India [ 14 ].
In the Philippines, the Inter-Agency Task Force on Emerging Infectious Diseases (IATF) was convened by the national government in January 2020 to monitor a viral outbreak in Wuhan, China. The first case of local transmission of COVID-19 was confirmed on March 7, 2020. Following this, on March 8, the entire country was placed under a State of Public Health Emergency. By March 25, the IATF released a National Action Plan to control the spread of COVID-19. A community quarantine was initially put in place for the national capital region (NCR) starting March 13, 2020 and it was expanded to the whole island of Luzon by March 17. The initial quarantine was extended up to April 30 [ 5 , 15 ]. Several quarantine protocols were then implemented based on evaluation of IATF:
Community Quarantine (CQ) refers to restrictions in mobility between quarantined areas.
In Enhanced Community Quarantine (ECQ), strict home quarantine is implemented and movement of residents is limited to access essential goods and services. Public transportation is suspended. Only economic activities related to essential and utility services are allowed. There is heightened presence of uniformed personnel to enforce community quarantine protocols.
Modified Enhanced Community Quarantine (MECQ) is implemented as a transition phase between ECQ and GCQ. Strict home quarantine and suspension of public transportation are still in place. Mobility restrictions are relaxed for work-related activities. Government offices operates under a skeleton workforce. Manufacturing facilities are allowed to operate with up to 50% of the workforce. Transportation services are only allowed for essential goods and services.
In General Community Quarantine (GCQ), individuals from less susceptible age groups and without health risks are allowed to move within quarantined zones. Public transportation can operate at reduced vehicle capacity observing physical distancing. Government offices may be at full work capacity or under alternative work arrangements. Up to 50% of the workforce in industries (except for leisure and amusement) are allowed to work.
Modified General Community Quarantine (MGCQ) refers to the transition phase between GCQ and the New Normal. All persons are allowed outside their residences. Socio-economic activities are allowed with minimum public health standard.
LGUs are tasked to adopt, coordinate, and implement guidelines concerning COVID-19 in accordance with provincial and local quarantine protocols released by the national government [ 16 ].
In this study, we identified economic and demographic factors that are correlated with epidemiological metrics related to COVID-19, specifically to the number of infected cases and number of deaths [ 17 , 18 ]. At the regional, provincial, and city levels, we investigated the localities that differ with the other localities, and determined the possible reasons why they are outliers compared to the average practices of the others.
We categorized the data into economic, health, and demographic components (See Table 1 ). In the economic setting, we considered the number of people employed and the number of work hours. The number of health facilities provides an insight into the health system of a locality. Population and population density, as well as age distribution and mobility, were used as the demographic indicators. The data (as of November 10, 2020) from these seven factors were analyzed and compared to the number of deaths and cumulative cases in cities, provinces or regions in the Philippines to determine the outlier.
The Philippine government’s administrative structure and the availability of the data affected its range for each factor. Regional data were obtained for the economic component. For the health and demographic components, data from cities and provinces were retrieved from the sources. Due to the NCR exhibiting the highest figures in all key components, an investigation was conducted to identify an outlier among its cities. The z -transform
where x is the actual data, μ is the mean and σ is the standard deviation were applied to normalize the dataset. Two sets of normalized data X and Y were compared by assigning to each pair ( x , y ), where x ∈ X and y ∈ Y , its Euclidean metric d given by d =( x − y ) 2 . Here, the Y ’s are the number of COVID-19 cases and deaths, and X ’s are the other demographic indicators. Since 95% of the data fall within two standard deviations from the mean, this will be the threshold in determining an outlier. This means that if a data pair ( x , y ) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y , the pair is assumed to be an outlier.
To identify a good outlier, a bias computation was performed. In this procedure, Y represents the normalized data set for the number of deaths or the number of cases while X represents the normalized data set for every factor that were considered in this study. The bias is computed using the metric
for all x in X and y in Y . To categorize a city, province, or region as a good outlier, the bias corresponding to this locality must exceed two standard deviations from the mean of all the bias computations between the sets X and Y .
Results and discussion
The data used were the reported COVID-19 cases and deaths in the Philippines as of November 10, 2020 which is 240 days since community lockdowns were implemented in the country. Figure 1 shows the different lockdowns implemented per province since March 15. It can be seen that ECQ was implemented in Luzon and major cities in the country in the first few weeks since March 15, and slowly eased into either GCQ or MGCQ as time progressed. By August, the most stringent lockdown was MECQ in the National Capital Region (NCR) and some nearby provinces. Places under MECQ on September were Iloilo City, Bacolod City, and Lanao del Sur, with the last province as the lone community to be placed under MECQ the month after. By November 1, 2020, communities were either placed under GCQ or MGCQ.
COVID-19 community quarantines in Regions III, IVA and VII
Comparison of economic, health, and demographic components and COVID-19 parameters
The economic, health and demographic components were compared to COVID-19 cases and deaths. These comparisons were done for different community levels (regional, provincial, city/metropolitan) (See Tables 2 , 3 , and 4 ). Figure 2 summarizes the correlation of components to COVID-19 cases and deaths at the regional level. In all components, correlations with other parameters to both COVID-19 cases and deaths are close. Every component except Residential Mobility and GRDP have slightly higher correlation coefficient for COVID-19 cases as compared to COVID-19 deaths.
Correlation of components to COVID-19 cases and deaths at the regional level
Among the components, the number of ICU beds component has the highest correlation with COVID-19 parameters. This makes sense as this is one of the first-degree measures of COVID-19 transmission. Population density comes in second, followed by mean hours worked and working population, which are all related to how developed the region is economy-wise. Regions having larger population density also have a huge working population and longer working hours [ 24 ]. Thus, having a huge population density implies high chance of having contact with each other [ 25 , 26 ]. Another component with high correlation to the cases and deaths is the number of doctors on quarantine, which can be looked at two ways; (i) huge infection rate in the region which is the reason the doctors got exposed or are on quarantine, and (ii) lots of doctors on quarantine which resulted to less frontliners taking care of the infected individuals. All definitions of mobility and the GDP are not strongly correlated to any of the COVID-19 measures.
In each data set, outliers were identified depending on their distance from the mean. For simplicity, we denote components that are compared with COVID-19 cases by (C) and with COVID-19 deaths by (D). The summary of outliers among regions in the Philippines is shown in Figs. 3 and 4 . Data is classified according to groups of component. In each outlier region, non-pharmaceutical interventions (NPI) implemented and their timing are identified.
Outliers among regions in the Philippines with respect to COVID-19 cases
Outliers among regions in the Philippines with respect to COVID-19 deaths
Region III is an outlier in terms of working population (C) and the number of ICU beds (C) (see Fig. 5 and Table 5 ). This means that considering the working population of the region, the number of COVID-19 infections are better than that of other regions. Same goes with the number of ICU beds in relation to COVID-19 deaths. Region III is comprised of Aurora, Bataan, Nueva Ecija, Pampanga, Tarlac, Zambales, and Bulacan. This good performance might be attributed to their performance especially on their programs against COVID-19. As early as March 2020, the region had been under a community lockdown together with other regions in Luzon. Being the closest to NCR, Bulacan has been the most likely to have high number of COVID-19 cases in the region. But the province responded by opening infection control centers which offer free healthcare, meals, and rooms for moderate-severe COVID-19 patients [ 27 ]. They have also implemented strict monitoring of entry-exit borders, organization of provincial task force and incident command center, establishment of provincial quarantine facilities for returning overseas Filipino workers, mandated municipal quarantine facilities for asymptomatic cases, and mass testing, among others [ 27 ]. Most of which have been proven effective in reducing the number of COVID-19 cases and deaths [ 28 ].
Outliers among the provinces in Luzon with respect to COVID-19 cases and deaths
Outliers among the provinces in Visayas with respect to COVID-19 cases and deaths
Outliers among the provinces in Mindanao with respect to COVID-19 cases and deaths
Region IV-A is an outlier in terms of population and working population (D) and doctors on quarantine (D) (see Fig. 5 and Table 5 ). Considering their population and working population, the COVID-19 death statistics show better results compared to other regions. Same goes with the number of doctors in the region which are in quarantine in relation to the reported COVID-19 deaths. This shows that the region is doing well in terms of decreasing the COVID-19 fatalities compared to other regions in terms of populations and doctors on quarantine. Region IV-A is comprised of Batangas, Cavite, Laguna, Quezon, and Rizal. Same with Region III, they have been under the community lockdown since March of last year. Provinces of the region such as Rizal have been proactive in responding to the epidemic as they have already suspended classes and distributed face masks even before the nationwide lockdown [ 29 ]. Despite being hit by natural calamities, the region still continue ramping up the response to the pandemic through cash assistance, first aid kits, and spreading awareness [ 30 ].
An interesting result is that NCR, the center of the country and the most densely populated, is a good outlier in terms of GRDP (C) and GRDP (D). Cities in the region launched various programs in order to combat the disease. They have launched mass testings with Quezon City, Taguig City, and Caloocan City starting as early as April 2020. Pasig City started an on-the-go market called Jeepalengke. Navotas, Malabon, and Caloocan recorded the lowest attack rate of the virus. Caloocan city had good strategies for zoning, isolation and even in finding ways to be more effective and efficient. Other programs also include color-coded quarantine pass, and quarantine bands. It is also possible that NCR may just have a very high GRDP compared to other regions. A breakdown of the outliers within NCR can be seen in Fig. 8 .
Outliers in the national capital region with respect to COVID-19 cases and deaths
Region VII is also an outlier in terms of population density (D) and frontliners (D) (see Fig. 6 and Table 5 ). This means that given the population density and the number of frontliners in the region, their COVID-related deaths in the region is better than the rest of the country. This region consists of four provinces (Cebu, Bohol, Negros Oriental, and Siquijor) and three highly urbanized cities (Cebu City, Lapu-Lapu City, and Mandaue City), referred to as metropolitan Cebu. This significant decline may be explained by how the local government responded after they were placed in stricter community quarantine measures despite the rest of the country easing in to more lenient measures. Due to the longer and stricter quarantine in Cebu, the lockdown had a greater impact here than in other areas where restrictions were eased earlier [ 31 ]. Dumaguete was one of the destinations of the first COVID case in the Philippines [ 32 ], their local government was able to keep infections at bay early on. Siquijor was also COVID-19-free for 6 months [ 33 ]. The compounded efforts of the different provinces in the region can account for the region being identified as an outlier.
Among the metropolitan cities, Davao came out as a good outlier in terms of population (C) and working population (C) (see Figs. 7 , 9 , and Table 5 ). This result may be attributed to their early campaign on consistent communication of COVID-19-related concerns to the public [ 34 ]. They were also able to set up transportation for essential workers early on [ 35 ].
Outliers among metropolitan areas in the Philippines with respect to COVID-19 cases and deaths
This study identified outliers in each data group and determined the NPIs implemented in the locality. Economic, health and demographic components were used to identify these outliers. For the regional data, three regions in Luzon and one in Visayas were identified as outliers. Apart from the minimum IATF recommended NPIs, various NPIs were implemented by different regions in containing the spread of COVID-19 in their areas. Some of these NPIs were also implemented in other localities yet these other localities did not come out as outliers. This means that one practice cannot be the sole explanation in determining an outlier. The compounding effects of practices and their timing of implementation are seen to have influenced the results. A deeper analysis of daily data for different trends in the epidemic curve is considered for future research.
Correlation tables, outliers and community quarantine timeline
Availability of data and materials.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, Xing X, Xiang N, Wu Y, Li C, Chen Q, Li D, Liu T, Zhao J, Liu M, Tu W, Chen C, Jin L, Yang R, Wang Q, Zhou S, Wang R, Liu H, Luo Y, Liu Y, Shao G, Li H, Tao Z, Yang Y, Deng Z, Liu B, Ma Z, Zhang Y, Shi G, Lam TTY, Wu JT, Gao GF, Phil D, Cowling BJ, Yang B, Leung GM, Feng Z. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. 2020; 382(13):1199–207.
Article CAS Google Scholar
Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, Druckenmiller H, Huang LY, Hultgren A, Krasovich E, Lau P, Lee J, Rolf E, Tseng J, Wu T. The effect of large-scale anti-contagion policies on the covid-19 pandemic. Nature. 2020; 584:262–67.
Anderson R, Heesterbeek JAP, Klinkenberg D, Hollingsworth T. Comment how will country-based mitigation measures influence the course of the covid-19 epidemic?Lancet. 2020; 395. https://doi.org/10.1016/S0140-6736(20)30567-5 .
Buhat CA, Torres M, Olave Y, Gavina MK, Felix E, Gamilla G, Verano KV, Babierra A, Rabajante J. A mathematical model of covid-19 transmission between frontliners and the general public. Netw Model Anal Health Inform Bioinforma. 2021; 10. https://doi.org/10.1007/s13721-021-00295-6 .
Ocampo L, Yamagishic K. Modeling the lockdown relaxation protocols of the philippine government in response to the covid-19 pandemic: an intuitionistic fuzzy dematel analysis. Socioecon Plann Sci. 2020; 72. https://doi.org/10.1016/j.seps.2020.100911 .
Weible C, Nohrstedt D, Cairney P, Carter D, Crow D, Durnová A, Heikkila T, Ingold K, McConnell A, Stone D. Covid-19 and the policy sciences: initial reactions and perspectives. Policy Sci. 2020; 53:225–41. https://doi.org/10.1007/s11077-020-09381-4 .
Article Google Scholar
Wibbens PD, Koo WW-Y, McGahan AM. Which covid policies are most effective? a bayesian analysis of covid-19 by jurisdiction. PLoS ONE. 2020. https://doi.org/10.1371/journal.pone.0244177 .
Mintrom M, O’Connor R. The importance of policy narrative: effective government responses to covid-19. Policy Des Pract. 2020; 3(3):205–27. https://doi.org/10.1080/25741292.2020.1813358 .
Chin T, Kahn R, Li R, Chen JT, Krieger N, Buckee CO, Balsari S, Kiang MV. Us-county level variation in intersecting individual, household and community characteristics relevant to covid-19 and planning an equitable response: a cross-sectional analysis. BMJ Open. 2020; 10(9). https://doi.org/10.1136/bmjopen-2020-039886 .
OECD. The territorial impact of COVID-19: managing the crisis across levels of government. 2020. https://www.oecd.org/coronavirus/policy-responses/the-territorial-impact-of-covid-19-managing-the-crisis-across-levels-of-government-d3e314e1/#biblio-d1e5202 . Accessed 20 Feb 2007.
White ER, Hébert-Dufresne L. State-level variation of initial covid-19 dynamics in the united states. PLoS ONE. 2020; 15. https://doi.org/10.1371/journal.pone.0240648 .
Lin S, Huang J, He Z, Zhan D. Which measures are effective in containing covid-19? — empirical research based on prevention and control cases in China. medRxiv. 2020. https://doi.org/10.1101/2020.03.28.20046110 . https://www.medrxiv.org/content/early/2020/03/30/2020.03.28.20046110.full.pdf .
Mei C. Policy style, consistency and the effectiveness of the policy mix in China’s fight against covid-19. Policy Soc. 2020; 39(3):309–25. https://doi.org/10.1080/14494035.2020.1787627. http://arxiv.org/abs/https: //doi.org/10.1080/14494035.2020.1787627.
Dutta A, Fischer HW. The local governance of covid-19: disease prevention and social security in rural india. World Dev. 2021; 138:105234. https://doi.org/10.1016/j.worlddev.2020.105234 .
Vallejo BM, Ong RAC. Policy responses and government science advice for the covid 19 pandemic in the philippines: january to april 2020. Prog Disaster Sci. 2020; 7:100115. https://doi.org/10.1016/j.pdisas.2020.100115 .
Inter-Agency Task Force for the Management of Emerging Infectious Diseases. Omnibus guidelines on the implementation of community quarantine in the Philippines. 2020. https://doh.gov.ph/node/27640 . Accessed 20 Feb 2020.
Roy S, Ghosh P. Factors affecting covid-19 infected and death rates inform lockdown-related policymaking. PloS ONE. 2020; 15(10):0241165. https://doi.org/10.1371/journal.pone.0241165 .
Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V. Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the covid-19 epidemic in france under lockdown: a population-based study. Lancet Digit Health. 2020; 2(12):638–49.
Department of Health. COVID-19 tracker. 2020. https://doh.gov.ph/covid19tracker . Accessed 25 Nov 2020.
Authority PS. Philippine population density (based on the 2015 census of population). 2020. https://psa.gov.ph/content/philippine-population-density-based-2015-census-population . Accessed 11 Apr 2020.
Google. COVID-19 community mobility report. 2020; https://www.google.com/covid19/mobility?hl=en. Accessed 25 Nov 2020.
Authority PS. Labor force survey. 2020. https://psa.gov.ph/statistics/survey/labor-and-employment/labor-force-survey?fbclid=IwAR0a5GS7XtRgRmBwAcGl9wGwNhptqnSBm-SNVr69cm8sCVd9wVmcoKHRCdU . Accessed 11 Apr 2020.
Authority PS. https://psa.gov.ph/grdp/tables?fbclid=IwAR3dKvo3B5eauY7KcWQG4VXbuiCrzFHO4b-f1k5Od76ccAlYxUimUIaqs94 . Accessed 11 Apr 2020. 2020.
Peterson E. The role of population in economic growth. SAGE Open. 2017; 7:215824401773609. https://doi.org/10.1177/2158244017736094 .
Buhat CA, Duero JC, Felix E, Rabajante J, Mamplata J. Optimal allocation of covid-19 test kits among accredited testing centers in the philippines. J Healthc Inform Res. 2021; 5. https://doi.org/10.1007/s41666-020-00081-5 .
Hamidi S, Sabouri S, Ewing R. Does density aggravate the covid-19 pandemic?: early findings and lessons for planners. J Am Plan Assoc. 2020; 86:1–15. https://doi.org/10.1080/01944363.2020.1777891 .
Philippine News Agency. Bulacan shares anti-COVID-19 best practices. 2020. https://mb.com.ph/2020/08/16/bulacan-shares-anti-covid-19-best-practices/ . Accessed Mar 2020.
Buhat CA, Villanueva SK. Determining the effectiveness of practicing non-pharmaceutical interventions in improving virus control in a pandemic using agent-based modelling. Math Appl Sci Eng. 2020; 1:423–38. https://doi.org/10.5206/mase/10876 .
Hallare K. Cainta, Rizal suspends classes, distributes face masks over coronavirus threat. 2020. https://newsinfo.inquirer.net/1238217/cainta-rizal-suspends-classes-distributes-face-masks-over-coronavirus-threat . Accessed Mar 2020.
Relief International. Responding to COVID-19 in the Aftermath of Volcanic Eruption. 2020. https://www.ri.org/projects/responding-to-covid-19-in-the-aftermath-of-volcanic-eruption/. Accessed Mar 2020.
Macasero R. Averting disaster: how Cebu City flattened its curve. 2020. https://www.rappler.com/newsbreak/explainers/how-cebu-city-flattened-covid-19-curve/ . Accessed Mar 2020.
Edrada EM, Lopez EB, Villarama JB, Salva-Villarama EP, Dagoc BF, Smith C, Sayo AR, Verona JA, Trifalgar-Arches J, Lazaro J, Balinas EGM, Telan EFO, Roy L, Galon M, Florida CHN, Ukawa T, Villaneuva AMG, Saito N, Nepomuceno JR, Ariyoshi K, Carlos C, Nicolasor AD, Solante RM. First covid-19 infections in the philippines: a case report. Trop Med Health. 2020; 48(30). https://doi.org/10.1186/s41182-020-00218-7 .
Macasero R. Coronavirus-free for 6 months, Siquijor reports first 2 cases. 2020. https://www.rappler.com/nation/siquijor-coronavirus-cases-august-2-2020 . Accessed Mar 2020.
Davao City. Mayor Sara, disaster radio journeying with dabawenyos. 2020. https://www.davaocity.gov.ph/disaster-risk-reduction-mitigation/mayor-sara-disaster-radio-journeying-with-dabawenyos . Accessed Mar 2020.
Davao City. Davao city free rides to serve GCQ-allowed workers. 2020. https://www.davaocity.gov.ph/transportation-planning-traffic-management/davao-city-free-rides-to-serve-gcq-allowed-workers/ . Accessed Mar 2020.
JFR is supported by the Abdus Salam International Centre for Theoretical Physics Associateship Scheme.
This research is funded by the UP System through the UP Resilience Institute.
Authors and affiliations.
Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Laguna, Philippines
Dylan Antonio S. Talabis, Ariel L. Babierra, Christian Alvin H. Buhat, Destiny S. Lutero, Kemuel M. Quindala III & Jomar F. Rabajante
University of the Philippines Resilience Institute, University of the Philippines, Quezon City, Philippines
Faculty of Education, University of the Philippines Open University, Laguna, Philippines
Jomar F. Rabajante
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All authors are involved in drafting the manuscript and in revising it. The author(s) read and approved the final manuscript.
Correspondence to Dylan Antonio S. Talabis .
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Not applicable. We used secondary data. These are from the public database of the Philippine Department of Health ( https://www.doh.gov.ph/covid19tracker ) and Philippine Statistics Authority Census ( https://psa.gov.ph )
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S. Talabis, D.A., Babierra, A.L., H. Buhat, C.A. et al. Local government responses for COVID-19 management in the Philippines. BMC Public Health 21 , 1711 (2021). https://doi.org/10.1186/s12889-021-11746-0
Received : 19 April 2021
Accepted : 30 August 2021
Published : 21 September 2021
DOI : https://doi.org/10.1186/s12889-021-11746-0
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