Article by Fernan Talamayan.

Abstract: The May 2020 report by the Committee for the Coordination of Statistical Activities on the social and economic impact of the Covid-19 outbreak validates Milan and Treré’s (April 2020) claim that the Global South is “virtually absent” from the “number-based narration of the pandemic.” Although there was an attempt to shed light on the potential ramifications of the pandemic on the Global South, the volume and variety of data presented in the report made the disparity between the Global North and Global South’s capacity to amass data apparent. The lack of data on the spread of Covid-19 in the Global South causes a plethora of problems, as it impairs the region’s ability to develop pragmatic containment plans and implement adequate economic, health, and social solutions. To this end, this article shares the disquieting situation in the Philippines. It provides examples of how the lack of data affects people’s physical and mental health and contributes to government (in)decisions.

Keywords: COVID-19, Global South, statistics, data divide, data inequality, Philippines

Header image “A street vendor, Manila” by International Labour Organization (ILO) Asia-Pacific is licensed under CC BY-NC-ND. The ILO (2020, May 1) warned that “workers in the informal economy stand in immediate danger of having their livelihoods destroyed as an impact of COVID-19.”

Latest Statistics from the UN

In May 2020, the United Nations (through the Committee for the Coordination of Statistical Activities, or CCSA) and its partner organizations in the international statistics community, published a report that provided impartial data and statistics on the economic and social impact of the Covid-19 pandemic. The report covered several aspects of public and private life, “from economic and environmental fluctuations to changes that affect individuals in terms of income, education, employment” (“How Covid-19 is changing the world,” 2020, May 14). The report aimed to encourage fact-based planning against Covid-19, particularly in developing countries that are in dire need of pragmatic containment and impact management strategies.

Based on said report, as of April 30, 2020, “Europe had the most cases of confirmed infection at 1,406,899, followed by the Americas at 1,246,190.” Following these regions were the Eastern Mediterranean (181,119), the Western Pacific (147,743), and South-East Asia (52,266). Africa, meanwhile, had only 24,713 reported cases (CSSA, 2020, p. 8). CSSA (2020) also informed that confirmed Covid-19 cases had exceeded 100,000 in the United States (1,003,974), Spain (210,773), Italy (201,505), the United Kingdom (161,149), Germany (157,641), France (125,464), and Turkey (114,653) (p. 8).

CCSA_COVID19_Report_p-9
Figure 1. CCSA’s May 2020 data on the confirmed number of COVID-19 cases by region (licensed under CC BY 3.0 IGO).

While such figures portray factuality, statistical data stating that Europe and the Americas had the largest numbers of Covid-19 cases may imply a dozen other things. At face value, they show how severely the Global North has been struck and how unprecedented the statistics are. However, as they present numbers from the Global North alongside Global South without clarifying the nuances in the regions’ data collection capabilities, such statistics could generate an “incomplete” representation of the problem.

To be clear, this article does not intend to discredit CCSA’s report; the publication of the report is truly commendable considering the extreme lockdown regulations and other limiting conditions across the globe. Also, this article does not intend to deny the usefulness of such quantifications in terms of the impact of Covid-19 on the global economy. To note the “incompleteness” of these statistical data is to recognize the Global South’s inability to sufficiently translate the Covid-19 problem into quantifiable knowledge. The statistics suggest the Global South’s limited testing capacity as several Covid-19 cases go untraced and undetected in many developing countries. They also hint at how weak and incompetent some of the region’s healthcare systems and governments are. Thus, for such data to be useful in devising viable solutions to several socio-economic problems caused by the pandemic, researchers and policymakers should go beyond data universalism (Milan & Treré, 2019) cognizant of the conditions and limitations in the Global South.

The politics of data

Milan and Treré (2020) pointed out that if there is anything that a “number-based narration of the pandemic” has highlighted, it is the “widening data divide” between the Global North and the Global South.

The Global South being a laggard in data collection is nothing new. But what the pandemic did is it magnified the existence of a “data gap”—a product of “the actual (in)ability of many countries in the South to test their population for the virus, and to produce reliable population statistics in general” (Milan & Treré, 2020). Since these numbers are “deeply ingrained in their socio-economic and political geography” (Milan & Treré, 2020), they also reflect the gap between the Global North and the Global South in terms of conducting contact-tracing and mass testing. Consequently, said gap can determine the possible outcomes of these regions’ efforts to stop or slow virus transmission. Extending Milan and Treré’s thesis, it may also be argued that a data gap can represent a government’s lack of commitment to transparency and democracy.

Despite the positive results of extensive data collection and monitoring in mitigating the impact of the pandemic (i.e., Taiwan’s community-based surveillance and data-driven face mask supply chains, New Zealand’s extensive testing and contact tracing operation), several governments in the South remain unable to recognize or address the need to increase their countries’ statistical and digital capacity. This could be attributed to a host of issues such as lack of resources, inadequate digital and health infrastructure, and other critical requirements to provide data-driven solutions. It may have also been caused by a government’s politicization of data or the presentation of numbers that serves the political agenda of a state.

Data-driven experience, data-driven empathy

If numbers, graphs, and data shape people’s experience of the pandemic (Aula, 2020), it would be curious to see how the lack of such could affect people’s understanding of the health crisis. In the same way, conflicting, incomplete, or misleading Covid-19 data merit analysis as they affect how the public discourse on the pandemic is framed (Kristian, 2020). Further, if human decisions govern data and its visualization (Kennedy, 2020), and if certain biases get coded in policies and technologies (Benjamin, 2019), attention must also be paid to data’s politicization. This implies an identification of “what data is known but not shared” or “who is included and excluded in data” (Kennedy, 2020).

That said, solutions to the Global South’s issues on data are urgently needed as numbers influence people’s “ability to care, share empathy, and donate to relief efforts and emergency services” (Milan & Treré, 2020). More importantly, numbers represent an issue “in both state policies and people’s imaginaries” (Milan & Treré, 2020). If numbers are conditions for a problem’s existence, incomplete data could potentially imply an “inexistence” of a problem—and this could lead a population into a false sense of security and spell disaster, especially in countries that have unequal access to medical care.

The Philippines during the Covid-19 pandemic

The Philippine experience offers an example of how the lack of data can lead to inapt government responses to the pandemic. It can also cast light on the social and health repercussions of data unavailability and statistical incapacity in the Global South.

The Philippine Department of Health (DOH) recorded 20,626 COVID-19 cases in the country as of June 5, 2020. Out of the 20,626 cases, there were 4,330 recorded recoveries and 987 deaths. The bulk of cases came from the National Capital Region (NCR), which amounted to 11,656. The number of confirmed Covid-19 cases is expected to rise as the national government commits to expanding access to Covid-19 testing, especially in hard-hit areas.

These numbers are relatively low, compared to the Philippines’ more developed neighbor Singapore (36,922). However, in Southeast Asia, the Philippines ranks third in terms of the total number of confirmed cases and first in mortality rate which is 9.15 per million (see figure 2). The Philippines also has the lowest recovery rate in the region (Co, 2020).

covid-19-mortality-rate-SEA
Figure 2. Covid-19 mortality rate by country in Southeast Asia as of June 5, 2020. Taken from the Southeast Asia Covid-19 Tracker of the Center for Strategic and International Studies (CSIS). https://www.csis.org/programs/southeast-asia-program/southeast-asia-covid-19-tracker-0

It must be noted that these numbers do not necessarily manifest the “real-time” count of Covid-19 cases. Due to the “long and tedious” verification process (Yee, 2020) and the lack of trained disease surveillance officers in local government units and other reporting units such as medical facilities and hospitals (de la Cruz, 2020), the gap between the number of persons who tested positive for Covid-19 and the reported cases continues to grow each day. Further, because of the poor digital infrastructure and information system in the Philippines, the data gathering process remains paper-based—hence, the “12-day backlog in the outcome of samples from Covid-19 tests” (de la Cruz, 2020).

Apart from the DOH’s insufficient statistical capacity, the lack of Covid-19 testing in the country adversely impacts Filipinos’ perception of the official data. Borrowing the words of O’Neil (2020), many believe that “the number of infected is close to meaningless” for the reason that “only people who get tested can be counted, and there still aren’t enough tests” (O’Neil, 2020). Since the Philippines is yet to have a concrete mass Covid-19 testing program (Lalu, 2020), the scale of the pandemic becomes bloated in people’s imaginations, as people assume that the actual number of Covid-19 cases is substantially higher than the numbers reported by the government. And since the country has long been plagued by transparency and accountability issues, there are fears that the national government might be manipulating Covid-19 data (or “juking the stats” as O’Neil puts it) to cover up its incompetence.

Some people doubt the accuracy of data on Covid-19 mortality, believing that deaths are not reported consistently. Human Rights Watch (HRW) said on April 28 that “the Philippine government has not fully reported prison deaths,” and it raised concerns that Covid-19 might be “spreading more quickly and widely in the country’s detention facilities.” According to the report, “at least seven inmates have died in the Quezon City Jail and one in the Cavite Provincial Jail” since March 25, and it was difficult to “determine whether the deaths were Covid-19 related because of the absence of testing in the facilities and the government’s failure to report them.”

Discrepancies and incompleteness of data, which result from government incompetence and the lack of testing and contact-tracing capacity, contribute to people’s frustrations and anxieties. While the national government committed to increasing the country’s testing capacity after placing Luzon and other areas under a “highly militarized” lockdown (Aspinwall, 2020) on April 4, the government, until today, is yet to provide a concrete mass Covid-19 testing program (Mercado, 2020). This inability to provide reliable data and public service, accompanied by government pronouncement that “mass testing would be left to the private sector” (Lalu, 2020), leaves Filipinos fearing for their welfare and wellbeing. They worry that their sacrifice during the lockdown will have been in vain and that they will have to make it through the pandemic mostly on their own. Signs of mounting frustrations can be observed in several social media posts and memes, as they continuously demand proper contact tracking, isolation, and adequate treatment from the government.

Military officials secure quarantine checkpoints, Manila
Military officials secure quarantine checkpoints, Manila” by ILO Asia-Pacific is licensed under CC BY-NC-ND.

Like in many countries in the Global South, the pandemic exposed a number of deliberately ignored social problems in the Philippines. Despite the imagery of a “rising Philippines” created by Duterte’s numerous infrastructure projects, the pandemic further highlighted his administration’s consistent neglect of the welfare of those who live at the margins of the society. Apart from ordering the military to kill quarantine violators (Billing, 2020), the Duterte government’s policies are anti-poor in nature: social distancing in Manila slums is almost an impossibility due to overcrowded common areas; despite being targeted by the government’s social amelioration program, many Filipino families belonging to the most-vulnerable sectors fail to qualify for two cash payments of 5,000 to 8,000 PHP (99 to 158 USD); face masks were declared mandatory and yet it remains “an expense a few can afford” (Lopez, 2020).

Conclusion

Data about COVID-19 cases across the globe exhibit two critical issues: (1) the “widening data divide” between the Global North and the Global South, and (2) the “data gap” in the Global South, which is caused by the region’s already existing financial and infrastructural constraints and politicization of data. As analyzed, the Philippine case emphasizes the need to address the worsening “data gap” in the region.

To further qualify the claim that “numbers are the condition of existence of the problem” (Milan & Treré, 2020), this article argued that numbers signal a multitude of issues, as they frequently unmask problems other than what they initially intended to imply. Numbers also neither show an accurate picture nor tell the actual extent of a problem. The Philippine situation proved this point, as numbers can be understood as a manifestation of the government’s inability to count and an indication of what the government intends to project to its public.

Because of the poor information system and insufficient statistical capacity of a government, the perceived scale and threat of the pandemic reach far beyond the reported numbers. Such a gap between what is imagined and what gets counted influences people’s reaction to government policies and the pandemic. As such, the need to capacitate the Global South in terms of data collection has never been more relevant than it is now.

In the grand scheme of things, it is the most vulnerable countries from the Global South that suffer the “intensifying harm” of this pandemic (Goodman, Politi, Raj, Chutel, & Dahir, 2020). And since the identified issues on data and datafication sit on top of the Global South’s poor public health care system, the crisis hurts the vulnerable population hardest, as the pandemic aggravates their already precarious condition. For these reasons, efforts must be doubled to improve the Global South’s statistical capacity relative to public health.

REFERENCES

Aspinwall, N. (2020, April 30). Police abuse, prison deaths draw concern as Philippines tightens lockdown measures. The Diplomat. https://thediplomat.com/2020/04/police-abuse-prison-deaths-draw-concern-as-philippines-tightens-lockdown-measures/

Aula, V. (2020, May 15). The public debate around COVID-19 demonstrates our ongoing and misplaced trust in numbers. London School of Economics Impact Blog. https://bit.ly/misplaced-trust-in-numbers

Benjamin, R. (2019). Captivating Technology: Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life. Durham and London: Duke University Press.

Billing, L. (2020, April 16). Duterte’s response to the Coronavirus: “Shoot them dead.” Foreign Policy. https://foreignpolicy.com/2020/04/16/duterte-philippines-coronavirus-response-shoot-them-dead/

The Committee for the Coordination of Statistical Activities. (2020). How COVID-19 is changing the world: a statistical perspective. https://unstats.un.org/unsd/ccsa/documents/covid19-report-ccsa.pdf

Co, B. (2020, May 16). The Philippines now has the highest COVID-19 fatality rate and lowest recovery rate in ASEAN. ABS-CBN News. https://bit.ly/Philippines-highest-fatality-rate-in-ASEAN

Corona: New Zealand claims no community cases as lockdown eases. (2020, April 27). BBC News. https://www.bbc.com/news/world-asia-52436658

de la Cruz, J. M. (2020, May 22). 12-day backlog in Covid-19 tests still hounding DOH. Business Mirror. https://businessmirror.com.ph/2020/05/22/12-day-backlog-in-covid-19-tests-still-hounding-doh/

Eurostat. (2020, May 14). How Covid-19 is changing the world: a statistical perspective. https://bit.ly/eurostatcovid19

Goodman, P. S., Politi, D., Raj, S., Chutel, L. & Dahir, A. L. (2020, March 24). In World’s Most Vulnerable Countries, the Pandemic Rivals the 2008 Crisis. The New York Times. https://www.nytimes.com/2020/03/24/business/coronavirus-per-country-pandemic.html?auth=login-facebook

Kennedy, H. (2020, May 4). Simple data visualisations have become key to communicating about the COVID-19 pandemic, but we know little about their impact. London School of Economics Impact Blog. https://bit.ly/simple-data-visualisations-key-to-communicating-COVID19-pandemic

Kristian, B. (2020, April 3). The misleading certainty of the coronavirus graphic. The Week. https://bit.ly/misleading-certainty-of-the-coronavirus-graphic

Lalu, G. P. (2020, May 19). No mass testing? Opposition groups ask what was 2-month lockdown for. The Philippine Daily Inquirer. https://newsinfo.inquirer.net/1277709/no-mass-testing-opposition-groups-ask-what-was-the-two-months-lockdown-for#ixzz6NeqR4Yv4

Lopez, E. (2020, May 25). In Philippine slums, heat, hunger take a toll under lockdown. Reuters. https://bit.ly/inphilippineslums

Mercado, N. A. (2020, May 21). Duque admits no COVID-19 mass testing ever conducted since outbreak. The Philippine Daily Inquirer. https://newsinfo.inquirer.net/1278855/duque-admits-no-covid-19-mass-testing-ever-conducted-since-outbreak

Milan, S., & Treré, E. (2019). Big Data from the South(s): Beyond data universalism. Television & New Media, 20(4), 319-335.

Milan, S. & Treré, E. (2020, April 3). A widening data divide: COVID-19 and the Global South. openDemocracy. https://www.opendemocracy.net/en/openmovements/widening-data-divide-covid-19-and-global-south/

O’Neil, C. (2020, April 14). 10 Reasons to doubt the COVID-19 data. Bloomberg: Opinion. https://www.bloomberg.com/opinion/articles/2020-04-13/ten-reasons-to-doubt-the-covid-19-data

Philippines: Prison deaths unreported amid pandemic. (2020, April 28). Human Rights Watch. https://www.hrw.org/news/2020/04/28/philippines-prison-deaths-unreported-amid-pandemic

Taiwan Ministry of Health and Welfare. (2020, February 16). To strengthen community-based surveillance. https://www.mohw.gov.tw/cp-115-51555-2.html

Wang, J.C., Ng, Y.C., & Brook, R.H. (2020). Response to COVID-19 in Taiwan: Big Data analytics, new technology, and proactive testing. JAMA, 323(14):1341-1342. doi:10.1001/jama.2020.3151

Yee, J. (2020, May 24). DOH explains gap in COVID-10 data. The Philippine Daily Inquirer. https://newsinfo.inquirer.net/1280036/doh-gap-in-virus-data-explained

 

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