key: cord-0816252-wr2ib0cl
authors: Amit, A. M. L.; Pepito, V. C. F.; Gutierrez, B.; Rawson, T.
title: Tracking changes in reporting of epidemiological data during the COVID-19 pandemic in Southeast Asia: an observational study during the first wave
date: 2020-10-27
journal: nan
DOI: 10.1101/2020.10.23.20217570
sha: 6182eb6547614f3450d492f3563bdc2113f6f329
doc_id: 816252
cord_uid: wr2ib0cl

Background When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and how to effectively design interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods. Methods We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems. We further described the differences in how epidemiological data are reported across countries and timepoints, and the accessibility of epidemiological data. Findings Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the COVID-19 pandemic. However, changes in reporting were frequent and varied across data and countries. Changes in reporting rarely occurred for demographic data such as age and sex, while reporting shifts for geographic and temporal data were frequent. We also found that most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses. Interpretation Countries have different reporting systems and different capacities for maintaining consistent reporting of epidemiological data. As the pandemic progresses, governments may also change their priorities in data sharing. Our study thus highlights the importance of more nuanced analyses of epidemiological data of COVID-19 within and across countries because of the frequent shifts in reporting. Further, most countries provide data on a daily basis but not always in a readily usable format. As governments continue to respond to the impacts of COVID-19 on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and the implementation and evaluation of interventions. Funding The work was supported through an Engineering and Physical Sciences Research Council (EPSRC) (https://epsrc.ukri.org/) Systems Biology studentship award (EP/G03706X/1) to TR. This project was also supported in part by the Oxford Martin School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background 29

When a new pathogen emerges, consistent case reporting is critical for public health surveillance. 30

Tracking cases geographically and over time is key for understanding the spread of an infectious disease 31 and how to effectively design interventions to contain and mitigate an epidemic. In this paper we 32 describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and 33 highlight the impact of specific reporting methods. 34 35

We reviewed key epidemiological variables from various sources including a regionally comprehensive 37 dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the 38 epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems. We 39 further described the differences in how epidemiological data are reported across countries and 40 timepoints, and the accessibility of epidemiological data. 41 42

Our findings suggest that countries in Southeast Asia generally reported precise and detailed 44 epidemiological data during the first wave of the COVID-19 pandemic. However, changes in reporting 45 were frequent and varied across data and countries. Changes in reporting rarely occurred for 46 demographic data such as age and sex, while reporting shifts for geographic and temporal data were 47

Research in context data-driven research, highlighting areas and time periods where particular data feeds are likely to be 97 particularly biased or data-sparse. We are also able to recommend, based upon our findings, prioritising 98 the use of the early-case histories of specific countries for the calculation of demographic-specific 99 disease parameters. By highlighting particular regions where specific data are available, such as travel 100 history, hospitalisation times and symptom-tracking, we are also able to identify ideal further topics of 101 research in the ongoing attempts to fight the spread of COVID-19. 102

We reviewed the data of the Open COVID-19 Data Working Group's centralised repository and other 105 relevant data sources to compare the reporting systems of 11 Southeast Asian countries. We found that 106 governments frequently changed the type of data and level of detail reported as the pandemic 107 progressed. Our study thus highlights the importance of more nuanced analyses of epidemiological 108 COVID-19 data within and across countries because of the frequent shifts in reporting. Further, most 109 countries provide data on a daily basis but not always in a readily usable format. As governments 110 continue to respond to the impacts of COVID-19 on health and the economy, data sharing also needs to 111 be prioritised given its foundational role in policymaking, and the implementation and evaluation of 112 To effectively respond to public health emergencies, there is a need for timely and accurate reporting 126 of statistics and data sharing as highlighted in the recent Ebola and Zika epidemics. [1] [2] [3] [4] To this end, the 127

Principles for Data Sharing in Public Health Emergencies consisting of timeliness, ethics, equitability, 128 accessibility, transparency, fairness, and quality have been developed and introduced. 3,5,6 As of writing, 129 only one study on data sharing during disease outbreaks among Southeast Asian countries has been 130 carried out. 7 The study evaluated data quality and timeliness of outbreak reporting in Cambodia, Lao 131 PDR, Myanmar, and Vietnam for dengue, food poisoning and diarrhea, severe diarrhea, diphtheria, 132 measles, H5N1 influenza, H1N1 influenza, rabies, and pertussis. Further, it highlighted the broad 133 differences observed in the data quality and timeliness between participating countries, concluding that 134 any international data-curating attempts must be versatile enough to accomodate this. 135

In the ongoing COVID-19 crisis, government organisations, public health agencies, and research groups 137 are responding to the call for rapid data sharing by providing data and curating detailed real-time 138 databases that are readily and publicly accessible. 8-11 Data from various groups have informed more 139 than 100,000 papers on COVID-19. 12 Despite progress in reporting and sharing data, several challenges 140 remain. First, there are ethical and privacy considerations that need to be balanced carefully against the 141 potential impact of open data sharing. Second, there is a clear lack of capacity and often appropriate 142 computational infrastructure that may make data sharing in real time unfeasible and burdensome. 3,4 143

Such challenges may result in changes in the quality and detail of data reporting between and within 144 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint countries over time as their respective health systems become increasingly overwhelmed. 11 These shifts 145 in reporting provide a challenge for accurately comparing epidemiological situations between countries. 146 In China, for example, it has been shown that changes in reporting have impacted modelling results of 147 the transmission parameters of COVID-19. 13 Further, as the pandemic progresses and epidemiological 148 information becomes increasingly less available, analyses of detailed case counts that cover the entire 149 duration of the epidemic may not be feasible. 10 150 151

To our knowledge, this is the first study to describe the ways in which various countries in a geographic 152 region report COVID-19 data, and how the detail of data reporting changed over time. We reviewed 153 detailed epidemiological data from Southeast Asian countries and tracked how countries' reporting of 154 COVID-19 data has shifted. We further evaluated differences in reporting between countries and 155 described the accessibility of epidemiological data during the first wave in 2020. This study is 156 descriptive and does not seek to evaluate health systems reporting. However, by providing these types 157 of information, researchers may be able to conduct better and more nuanced analyses of epidemiological 158 data of COVID-19. By showing changes in reporting, we hope to provide insights on how data should 159 be viewed and analysed. 160 161

We conducted an observational study to describe and track changes in reporting of epidemiological data 164 during the COVID-19 pandemic in 11 countries in Southeast Asia, namely Brunei, Cambodia, 165

Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, and 166

Vietnam. Such a design allows us to compare the data reporting practices between different countries 167 through time as the pandemic progresses. 14 168 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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We focused on reporting mechanisms of individual level COVID-19 data from the aforementioned 11 170 countries in Southeast Asia. The region is characterized by archipelagos and comprises more than 8·0% 171 of the world's population. During the first wave of the pandemic, these 11 countries contributed about 172 1·3% of the cases to the global count of more than 2·3 million cases on April 20. The collection of data on these variables mirrors the minimum data to be collected for a line list of 183 pandemic influenza cases obtained from surveillance systems, as suggested by the WHO. 15 Other 184 sources, such as the interactive dashboard by Johns Hopkins University, 9 do not provide detailed 185 individual-level information and hence were not used in this study. bulletins. In addition, we reviewed data from news agencies, pre-prints and peer-reviewed research 191 articles that contained information on COVID-19 cases in the country. We reviewed all possible 192 publicly available data sources from the date when the first confirmed case was reported in the country, 193 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint and up to April 20. We documented how key information was reported and how it changed through 194

Shifts in reporting of epidemiological data during the first wave 197

We documented trends and changes in how key epidemiological variables were reported by 11 198

Southeast Asian countries throughout the study period from January 23 to April 20. Time periods were 199 defined by specific milestones in each country's data reporting. The first time period or 'first reporting 200 of cases' for all countries was the date at which the country reported its first COVID-19 case. 201

Meanwhile, the 'first change in reporting' was the time when the information format was changed from 202 the first report based on available data during the study period. Any further changes in the level of 203 detail, also referred in this paper as granularity for geographic data and precision for both demographic 204 and temporal data, in the reporting of any of the epidemiological variables were considered as a 'change 205 in reporting' and were noted as a subsequent time period (Table 1 ). The 'last observed change in 206

reporting' was the last documented change up to April 20. We also noted the number of cases in each 207 timepoint. In this paper, we only present results on the 'first reporting of cases' (T0), 'first observed 208 change in reporting' (T1), and 'last observed change in reporting' (T2). 209 210

We explored the differences in reporting of demographic, geographic, and temporal data across 212 countries at three key timepoints: at the time they first reported cases (T0), at the time when the reporting 213 first changed (T1), and at the last observed change in reporting (T2). Any change in the level of 214 granularity or precision in reporting is noted. We present these differences for each epidemiological 215 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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We described the accessibility of COVID-19 data in the region by documenting and providing a list of 219 sources for each country in different formats, including: government trackers and dashboards that report 220 close to real-time data, downloadable PDF reports on cases, downloadable CSV files for cases, and 221

Github repository for cases. In addition, we noted the frequency of data updates by government and 222 public health agencies in the country. 223 224

The funders had no role in study design, data compilation, data analysis, data interpretation or writing 226 of the report. All authors had access to the data, and had final responsibility for the decision to submit 227 for publication. 228 229

The first Southeast Asian country to report a COVID-19 case was Thailand on January 23. Singapore, 232

Malaysia, Cambodia, Vietnam and the Philippines subsequently reported cases on or before the WHO 233 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint terms of the level of granularity and precision in reporting data were also noted for the following 241 countries: Philippines eventually reported comorbidities for some patients, Singapore and Vietnam 242 eventually reported data on occupation, and Timor-Leste eventually reported travel history data. As 243 case numbers increased, several countries provided less detailed information. By March 15, when 96 244 cases had been identified, Indonesia ceased reporting individual-level data and switched to aggregate 245 data (i.e., number of cases per day). Timor-Leste followed by April 15, when it had 8 recorded cases. 246

The first and the last changes in reporting were the same for Indonesia and Brunei, while Myanmar was 247 the only country that consistently reported individual-level COVID-19 epidemiologic data since 248 reporting its first two cases on March 23 until April 20. 249 250

There were minimal changes in the reporting of demographic data among countries. The majority of 252 countries reported age and sex except for Timor-Leste, and only Indonesia shifted from a more precise 253

reporting of age and sex to less detailed reporting (Figures 2a and 2b) . We observed more changes in is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint Thailand initially reported province-level addresses and Vietnam initially reported city-level addresses; 266 eventually all three countries reported precise address data. There were less differences observed for 267 travel location data reporting across countries, but also more shifts observed across time (Figure 3b) . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint of symptom onset. Lao PDR repeatedly shifted between reporting of dates to providing no such 293 information. 294

Most of the governments in Southeast Asia instituted infrastructures and guidelines for the 297 dissemination of COVID-19 data, and provided free access to the public. Brunei's COVID-19 data are 298 publicly available but the individual requesting the information needs to provide passport information 299 and contact number to gain access to the database. During the study period, all countries had 300 government trackers and dashboards except for Timor-Leste (Table 2) 

Responding to calls for data sharing and transparency, most governments in Southeast Asia established 308 publicly available sources of COVID-19 individual-level information. This commitment to data sharing 309 and reporting allowed the comparison of the different data reporting practices of the countries in the 310 region. We found that countries in Southeast Asia have different reporting practices since the start of 311 the pandemic and during the first months of its progression. Overall, reporting of epidemiological data 312 in Southeast Asia is precise and detailed. Many variables were consistently maintained throughout the 313 initial outbreak period, but those with changes in reporting started early with case counts as low as four 314

to as high as 136. There was little to no change in reporting of demographic data while changes in 315

reporting of geographic and temporal variables were frequent and unpredictable as the pandemic 316

progressed. Further, we find that changes in the level of precision in reporting does not only depend on 317 case numbers, but also on the policies and interventions implemented. Comparisons across countries 318 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint for different epidemiologic variables showed that national governments may shift to a less or more 319 precise reporting of data as dictated by the burden of COVID-19 in the communities and/or their 320 national response. As an example, Indonesia started reporting aggregate data less than two weeks after 321 their first case was reported. Their government did not implement a nationwide lockdown, but rather 322 focused on scaling up capacity, treatment of patients and supporting economic recovery. Conversely, 323

Lao PDR, Thailand and Vietnam reported more precise demographic and geographic data at the end of 324 the study period compared to how they reported their first cases. The national governments of these 325 countries established mechanisms to quickly identify and isolate cases and their contacts requiring 326 detailed contact tracing data. Our findings also show that most countries reported more precise 327 information towards the end of the study period, but some variables such as travel history location were 328 reported with less detail compared to the increased precision for domicile data. These trends in travel 329 history data highlight the shift in priorities of the governments in the region towards managing local 330 transmission. Southeast Asian countries implemented travel restrictions early, therefore having fewer 331 imported cases and less need for precise travel history data. 33 332 333 Data on dates of symptom onset, confirmation, admission, and death or discharge are important in 334 estimating disease burden and forecasting health service needs. Dates of confirmation and death or 335 discharge were reported consistently by most countries. This reflects the effective system of 336 governments to register all confirmed patients in their database upon entry and exit in the healthcare 337 system. However, we found that dates of symptom onset and hospital admission were no longer reported 338 at the end of the observation period. The reporting of less precise dates could be attributed to the 339 increasing incidence of COVID-19, which could have overwhelmed data reporting mechanisms of the 340 countries, particularly because individual patient follow-up requires symptom onset dates to be 341 accurately logged. Governments thus need to establish systems that allow accurate and fast reporting of 342 detailed temporal data. Lack of precision could adversely affect the quality of mathematical models and 343 other analyses, which are used to forecast demand for health services and make decisions. This 344 consequently impacts the responses to COVID-19 at a national and subnational level, which is of greater 345 concern among low-and middle-countries (LMICs) that have already fragile health systems. Our 346 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint findings provide insights on how different health systems respond to the pandemic. Consequently, these 347 could be used to guide how publicly available data is analysed, used, and interpreted. 348

Most countries reported COVID-19 data daily, with unclear reporting frequencies only being observed 350

for Brunei, Lao PDR, and Timor-Leste. These countries do not report new cases every day because of 351 the low number of new daily cases leading to days where no additional cases are confirmed. As they 352 only provide updates on days when new COVID-19 cases are confirmed, their frequency of providing 353 data updates on COVID-19 is thus irregular. Countries primarily reported individual-level data in either 354 HTML and PDF formats, which necessitates scraping and extraction before such data could be used in 355

analyses. During the study period, only Thailand provided a downloadable CSV format of their data. 356

Ready-to-use data formats are important as these allow the public and scientific community to rapidly 357 view and analyse country-specific information. 358

An important limitation of this study is the absence of an assessment on data quality. This evaluation 360

was not carried out because of the fast progression of the pandemic with corresponding rapid changes 361 in data reporting. The lack of an up-to-date and complete line list also prevents a thorough assessment 362 of data quality. Lastly and most importantly, an evaluation of data quality also requires the consideration 363 of other indicators such as flexibility, representativeness, data security and system stability to provide 364 a more accurate picture of health systems and disease surveillance systems. 7 These information are not 365 readily available and require more resources to be collected. Despite such caveats, however, this study 366 is the first to systematically describe and compare reporting of important epidemiological data for 367 COVID-19 across countries during the first wave. Our findings will allow researchers to conduct more 368 nuanced analyses using epidemiological data of COVID-19. 369

In conclusion, reporting systems in the region have been quickly established and countries provided 371 detailed individual-level data during the first wave. This pandemic highlights the critical role of timely, 372

accurate, and precise data sharing during outbreaks of global scale. Some concerns regarding data 373

sharing remain, such as data privacy and public criticisms. 3, 4 Given that sharing of data is needed for 374 . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint evidence-informed policies and interventions, maintaining and strengthening data reporting systems 375 should still be a priority of countries. [34] [35] [36] For the purposes of surveillance on emerging infectious 376 diseases, we recommend that governments coordinate data collection and reporting so that data are as 377 comparable as possible between countries. Countries may also benefit from reporting data in a fully 378 open access format that is readily available and in machine-readable formats to accommodate new 379 epidemics and context-specific information. Hopefully, more governments will come to share precise 380 data to allow more nuanced analyses. This will provide an opportunity to better understand the disease 381 and how best to respond to the pandemic. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint Tables and figures  Table 1 . Variables included in the study and the definitions of 'change in reporting' is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted October 27, 2020. ; https://doi.org/10.1101/2020.10.23.20217570 doi: medRxiv preprint Country changes the level of precision at which it reports its domicile data (e.g., precise location to province)

. CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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Each country may report less granular data indicated by a decreasing slope (red) or more granular data indicated 469 by an increasing slope (blue) consistently over time. Reporting may not be consistent across timepoints with shifts 470 between different levels of granularity (yellow) or reporting may not have changed at all during the study period 471 (grey). The levels of granularity are indicated for each epidemiological variable. All geographic data have five 472 levels of granularity/geographic resolution: none, country, province, city, and precise. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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