key: cord-0687585-q3wf5xmv authors: Djaafara, B. A.; Whittaker, C.; Watson, O. J.; Verity, R.; Brazeau, N. F.; Widyastuti, W.; Oktavia, D.; Adrian, V.; Salama, N.; Bhatia, S.; Nouvellet, P.; Sherrard-Smith, E.; Churcher, T. S.; Surendra, H.; Lina, R. N.; Ekawati, L. L.; Lestari, K. D.; Andrianto, A.; Thwaites, G.; Baird, J. K.; Ghani, A. C.; Elyazar, I. R.; Walker, P. G. title: Quantifying the dynamics of COVID-19 burden and impact of interventions in Java, Indonesia date: 2020-10-02 journal: nan DOI: 10.1101/2020.10.02.20198663 sha: cd0b65d1fb86504373c79f776f8979443e378edd doc_id: 687585 cord_uid: q3wf5xmv Measuring COVID-19 spread remains challenging in many countries due to testing limitations. In Java, reported cases and deaths increased throughout 2020 despite intensive control measures, particularly within Jakarta and during Ramadan. However, underlying trends are likely obscured by variations in case ascertainment. COVID-19 protocol funerals in Jakarta provide alternative data indicating a substantially higher burden than observed within confirmed deaths. Transmission estimates using this metric follow mobility trends, suggesting earlier and more sustained intervention impact than observed in routine data. Modelling suggests interventions have lessened spread to rural, older communities with weaker healthcare systems, though predict healthcare capacity will soon be exceeded in much of Java without further control. Our results highlight the important role syndrome-based measures of mortality can play in understanding COVID-19 transmission and burden. over 9 million and the main epicentre of the virus in Indonesia during early 2020. Three epidemiological indicators of COVID-19 obtained from (22) are used -reported cases, reported deaths of individuals confirmed to be COVID-19 positive prior to death, and the daily reported COVID-19 protocol (C19P) funerals. C19P funerals are conducted when, at the time of death, the deceased were either COVID-19 positive or had COVID-19 symptoms and were either yet to be tested or to receive results. Figure 2A shows the daily reported cases, deaths, test positivity ratios, and funerals with C19P in Jakarta. These data are transformed into inferred dates of symptom onset using estimates of the delays between onset and diagnosis, onset and death, and onset and funeral derived from anonymised individual-level data from confirmed COVID-19 patients in Jakarta (Figure 2B , also see Methods and Supplementary Figure S1 ). As of 2 nd March, when COVID-19 was first identified in Indonesia, we estimate that 31 (22-41 95% CrI) and 124 (107-139 95% CrI) confirmed deaths and C19P funerals (assuming all funerals represent deaths due to COVID-19) had symptom onset occurring before 2 nd March. Assuming an infection fatality ratio of 0.657%, estimated from China (23) and adjusted for the population age-structure in Jakarta, we estimate 7,920 (5,490-10,360 95% CrI) infections based on confirmed deaths (reflecting an assumption that all undiagnosed individuals with a C19P funeral would have tested negative) or 30,830 (26,550-34,960 95% CrI) based on C19P funerals (reflecting an assumption that all undiagnosed individuals with a C19P funeral would have tested positive) had occurred in Jakarta by 2 nd March. This suggests both substantial undetected initial transmission across the city as well as large discrepancies in the predicted burden of COVID-19 depending on the data source used. Estimates of the virus' dynamics and transmissibility are also sensitive to the data source utilised. Based on reported cases, we predict the epidemic to have peaked around mid-April, before rising again in September 2020 to a far higher level than previously experienced in Jakarta ( Figure 2B) . The testpositivity rate declined in the first half of 2020, indicating increased testing rates and case-ascertainment, which further complicate interpretation of trends based on case data alone. The effects of this bias are visible when contrasted with the data on C19P funerals, which suggest the peak in infections likely occurred in mid-March, and that current infection levels are only now at levels comparable to their initial peak. The epidemic trajectory suggested by the officially reported deaths (requiring a positive COVID-19 test), which are also likely to be biased by changes in testing rates but to a lesser extent than cases, fall between those of cases and C19P funerals. To better quantify and understand the dynamics of spread within Jakarta, we utilise a branching-process based methodology to generate estimates of the reproduction number over time (ܴ ௧ ) (24). Three different estimates are obtained based on the three reconstructed time-series of symptom-onset derived from cases, deaths, and C19P funerals. All support the substantial impact of NPIs: we estimate China (26)), whereby estimates of ܴ ௧ during periods of equivalent levels of mobility during AKB are lower than estimates obtained prior to AKB (Supplementary Figure S3) . We next sought to understand the dynamics of the epidemic's subsequent subnational spread across the six provinces of Java and explore how fine-scale heterogeneity in factors relevant to COVID-19 risk and transmission might influence the burden of the disease (including demography, healthcare capacity, and between-district mobility). Our results highlight substantial variation across the island in these risk factors, with the proportion of individuals over the age of 50 ( Figure 3A , an age above which the risk of COVID-19 mortality markedly increases (23)) varying by over 2.5-fold between districts, as well as substantial variation in the number of hospital beds per 1 thousand population. This latter difference is most pronounced when comparing the comparatively well-resourced Jakarta setting (2.22 beds per thousand population) to the poorer, more rural setting of Tasikmalaya in West Java (0.18 beds per thousand population) ( Figure 3B) . Patterns of between-district mobility outside of the window of the pandemic, estimated using mobile phone data over the period of 1 st May 2011 -30 th April 2012, highlight the extent to which these settings are connected. Between-district connectivity is particularly high during the Ramadan period, which is marked by the large-scale movement of the population from densely populated Jakarta to other more rural and less well-resourced regions where populations are typically older and thus more vulnerable to morbidity and mortality as a result of COVID-19 infection (Figures 3C and 3D ). In the absence of equivalent resolution C19P funeral data in other provinces, we applied our estimates of the relationship between mobility and ܴ ௧ obtained from Jakartan funeral data ( Figure 3E ) to trends in Google Mobility data from the remaining provinces in Java. These estimates indicate a large impact on transmission in all provinces coinciding with the PSBB period. However, they also suggest that only in Jakarta and Yogyakarta were measures sufficient to bring ܴ ௧ below 1 for a sustained period of time. Across all provinces, however, increases in mobility occurred either during early May (Banten, West Java, Central Java, and East Java) or alongside the establishment of the AKB in June (Jakarta and Yogyakarta), leading to corresponding increases in our estimates of ܴ ௧ ( Figure 3F ). To capture the subnational spread of the virus, we developed a meta-population model of SARS-CoV-2 transmission by extending an existing non-spatial model (28), with each population in the model representing either the city of Jakarta or one of the 113 remaining districts across Java. We parameterised the model using mobile phone data derived estimates of between-district mobility (during both Ramadan and non-Ramadan periods, Figures 3C and 3D) prior to the pandemic, data on districtlevel demographic structure, and our estimates of within-province ܴ ௧ . Figure 4A shows the district-level metapopulation model-based simulation results for COVID-19 deaths for our baseline scenario designed to capture COVID-19 dynamics in Java to date, with the model calibrated to match patterns of mortality in Jakarta by varying the degree of initial seeding of infection early in January. Table 1 shows a comparison between simulated trends in provinces outside of Jakarta for our model to both data on confirmed deaths and two additional, but incomplete, data sources for suspected deaths (a combination of both confirmed and probable COVID-19 deaths) in these provinces. These were weekly situation reports compiled by WHO (18), which provide estimates of suspected deaths from 13 th May onwards; and estimates of cumulative suspected deaths based on provincial COVID-19 data monitoring website compiled by an independent group KawalCOVID19 (29). Given our simplified assumption of a consistent relationship between mobility and However, the model appears to capture the approximate magnitude and timing of deaths across provinces during the PSBB period, with the epicenter shifting over time from Jakarta to satellite towns and other provincial capitals, and with Yogyakarta remaining least affected. Prior to the first week in June, the point of transition to the AKB, 'new-normal' period, our median baseline model estimates fall within the range between confirmed and suspected deaths of both WHO and provincial data in most provinces, with total suspected deaths falling well within the uncertainty bounds of the model for all provinces (Table 1 ). In addition to this baseline scenario (Scenario 1), we use our model to explore an additional four counterfactual scenarios across the six provinces ( Figure 4B ). These include a scenario assuming no movement reductions during the Ramadan period and similar ܴ ௧ values to our baseline (Scenario 2); a scenario assuming no movement reductions during the Ramadan period and ܴ ௧ values that are 75% of each district's ܴ value (Scenario 3); a scenario assuming no movement reductions during the Ramadan period and ܴ ௧ values equal to each district's ܴ value (Scenario 4); and an unmitigated scenario assuming no implementation of interventions across the course of the epidemic (Scenario 5). In total, our baseline scenario produces an estimated 3,700 (1,500-7,390, 95% uncertainty interval (UI)) deaths up to May 31st. This provides an estimate of 71,250 (34,400-136,000, 95% UI) deaths averted when compared to an effectively unmitigated epidemic with ܴ ௧ = 2 throughout this period (which we estimate would have resulted in 74,990 (36,360-142,750, 95% UI)) deaths). This number does not take into account the effects of healthcare services becoming overwhelmed (as shown by the negative values of the median number of hospital beds available per COVID-19 case needing hospitalisation under the unmitigated epidemic scenario, Figure 4C ) on both direct and indirect mortality, an impact which would likely have been sizable given the wider spread to more rural settings with more scarce healthcare provision in our unmitigated scenario (Figures 4D and 4E) . Our estimates also suggest that the relatively aggressive Ramadan travel restrictions are likely to have had a sizable impact on preventing wider spread to more vulnerable rural areas. Our baseline model (Scenario 1) increasingly over-predicts deaths in most provinces during the transitional PSBB and AKB 'new-normal period'. This is in line with our results suggesting a decoupling of within-province mobility from virus transmissibility over that period (observed across other settings (26), which could be related to individual-level changes in population behaviour such as increased maskwearing, social distancing, changing work patterns, etc.). The reported cases in Jakarta substantially differ from model estimates after the AKB (Supplementary Figure S4) , congruent with the province altering its testing strategy to more actively find cases and clusters of cases that include milder infections not requiring hospitalizations. Data from other provinces (which represent hospitalised cases only and do not include those identified through active case investigation) are more consistent with the model results. To better capture within-province transmission trends during the AKB and up to the present (and given current observed community spread in all provinces), we use the simpler non-spatial model to fit province-specific models to either reported deaths (from (14, 22)) or both reported and suspected deaths (the C19P funerals data in the case of Jakarta (22) and collated from (18) otherwise, see Methods and Supplementary Material) ( Figure 5A) . Outside of Jakarta, model estimates of the attack rate (cumulative proportion infected) are below 5% across all provinces. In Jakarta, by contrast, the fitting was more sensitive to our choice of metric: fitting to all suspected deaths increasing the estimated attack rate by a factor of 4 to 14.0% (12.7-15.8%, 95% CrI), relative to the 3.5% (3.1-4.1%, 95% CrI) estimated when restricting analysis to confirmed deaths alone ( Figure 5B) . Irrespective of the data sourced used, these estimates suggest attack rates across Java to be far short of putative herd immunity thresholds, with this threshold likely to range from 34.6% to 50% for an ܴ of 2 depending on assumptions surrounding heterogeneities in mixing and susceptibility (30). Moreover, our province-level point estimates for ܴ ௧ between 20th August -4th September ranged between 1.02-1.22 and 1.05-1.29 for confirmed and suspected deaths, respectively, indicating that burden is likely to continue to rise in the coming weeks. We explore a range of future scenarios in each province using combinations of three values of the 'reproduction number under control', ܴ , (i.e., the average number of secondary infections occurring from an index infection in a wholly susceptible population but accounting for the impact of currently implemented , representing levels of transmission estimated at the beginning of the epidemic. These scenarios, using a range of estimates of transmissibility already observed across Java to date, highlight how different assumptions about future changes in control-policy and population-level behaviour (and their subsequent impact on ܴ ) can produce a wide array of future epidemic trajectories ( Figure 5C ). Extrapolating recent trends in transmission in Jakarta (i.e., our 'current' scenario) suggests a situation where demand for health services will increasingly approach, and likely exceed, capacity in the coming weeks or months ( Figure 5D ) but to a much lesser extent than would occur in circumstances where ܴ further increased towards that estimated at the beginning of the epidemic (ܴ of 2). This scenario is largely mirrored elsewhere in Java, where, on average, current levels of infection are lower but where dedicated healthcare services are in shorter supply. Our results suggest that attempts to further suppress transmission, such as the recent re-imposition of PSBB in Jakarta in recent days, if successful in reducing ܴ below 1, could largely keep healthcare within capacity. However, this will not alleviate the need for the development of sustainable approaches to controlling transmission over the coming year: in both our current and suppression scenarios, we estimate returning to pre-pandemic levels of social interaction once burden has subsided to low levels (a 'Return to Normal' scenario, where ܴ ൌ 2 . 0 once burden has fallen below an average of 1 death per day over the course of a week) would risk a substantial subsequent, additional wave of infection across Java. Our uncertainty in the underlying burden to date is also a key contributor to our uncertainty in the future trajectory of the epidemic, particularly in Jakarta, which has the highest disparity between confirmed and suspected deaths. For example, our projection of current trends assuming all suspected deaths are due to COVID-19 suggests an imminent threat of the health services reaching capacity within the city. In contrast, our projection based solely upon confirmed deaths would suggest healthcare will remain within the capacity for longer, due to lower levels of currently active infection. However, this epidemic trajectory results in a larger and more prolonged peak lasting well into 2021, driven by much lower levels of accumulated immunity within the population. If current attempts to achieve suppression are successful, this uncertainty is likely to be of even higher importance in terms of understanding the level of control required to prevent a subsequent wave: assuming all suspected deaths are due to COVID-19, our suppression scenarios achieve a level of immunity where current levels of ܴ could be sustained without a major subsequent wave. By contrast, assuming only confirmed deaths are due to COVID-19 would imply much lower levels of immunity, producing a further subsequent wave in 2021 in our suppression scenario if ܴ returned to current levels. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted October 2, 2020. . Our analysis highlights the challenges in capturing the transmission dynamics during an emerging pandemic of a novel pathogen. Basing our epidemiological inference on deaths (both diagnostically confirmed and using C19P funeral data as an indirect measure of symptomatically suspected), we estimate substantial circulation of SARS-CoV-2 in Jakarta prior to the date of the first confirmed COVID-19 case, as well as an earlier and more sustained decline in transmissibility in response to NPIs. In addition to the different dynamics of the reproduction number suggested when comparing inference using cases and deaths, we demonstrate that alternative sources of mortality data can help supplement our understanding of the historical spread of the epidemic. This is particularly evident in Jakarta, where the estimated attack rate varies by four-fold depending on the extent to which the deaths of individuals assessed to have been displaying COVID-19 symptoms but yet to receive a test result at the point of death (and subsequent burial) are assumed attributable to the infection. This undiagnosed COVID-19 burden in Jakarta and spread to date has important consequences for our projections of the current trajectory of the epidemic and the likely longer-term impact of interventions such as the re-establishment of measures aimed at suppressing transmission in Jakarta in recent days. These results highlight the critical importance of placing recently observed COVID-19 dynamics within the context of previous patterns of the spread of the disease to more accurately understand the future. In many settings, syndromic-based measures of mortality such as C19P funeral data are not routinely available; however, verbal autopsy provides a well-established retrospective means with which to conduct such inference. Given the scarcity of serological surveys in many settings, such approaches are likely to yield vital data with which to improve our understanding of the future trajectory of the epidemic and the likely impact of different options for control. C19P funerals and other measures of suspected mortality provide an alternative lens though which to understand the COVID-19 burden but do not allow precise measurement. In the absence of a confirmed diagnosis, the proportion of these individuals who were infected will always be unknown and liable to vary in time and space, as will the extent to which measures of suspected deaths represent all deaths of individuals displaying COVID-19 symptoms. However, irrespective of the data source used, our results highlight the impact of NPIs employed across Java, including substantial COVID-19 associated mortality avoided through swift implementation of control measures in response to the spread of the virus. In particular, our analyses indicate that imposition of stringent measures in Jakarta combined with restrictions on mobility patterns during Ramadan are likely to have delayed the seeding of epidemics to rural, more vulnerable, areas of the island. Given subsequent upwards trends in transmission, this could have potentially accelerated epidemics in such areas beyond currently observed levels. However, current best estimates suggest ܴ ௧ is now above 1 in all provinces, and our results emphasise the likely worsening of the epidemic across Java. This is consistent with recent observations that healthcare demand in Jakarta is currently heading beyond that observed during the initial peak and threatening to outstrip capacity in the near future (21). Our results suggest that the re-imposition of PSBB in Jakarta, beginning on the 14 th of September, if successful in suppressing transmission, could largely prevent capacity being exceeded. However, based on current trends, the remaining provinces in Java are likely to be faced with similar choices between re-imposition of suppression measures or exceeded healthcare demand in the coming weeks and months. Whilst we have attempted to propagate this past uncertainty throughout our analysis of future scenarios, there remain numerous important uncertainties that need to be considered when interpreting these simulations. In particular, both the level of population-level immunity acquired within each scenario (due to factors such as the strength and duration of individual responses) (31, 32) and the level of immunity required to achieve herd immunity are not yet well understood (30). However, none of the suppression scenarios considered here, assuming perfect and durable immunity to reinfection, achieve levels of immunity high enough to preclude a subsequent wave if life were to return to normal and transmissibility were to rise to levels observed at the beginning of the epidemic (ܴ ௧ = 2). We cannot capture the indirect impact of either suppression measures or that of health systems becoming stretched and/or overwhelmed in this analysis, though both are likely to be high and need to be considered carefully by decision-makers. More broadly, our results highlight that in the short-medium term, levels of population immunity are unlikely to develop to the extent that life in Java can return to prepandemic levels of normalcy without leading to substantial incremental increases in mortality. This highlights the need for long-term strategic planning and the urgent need for new tools such as therapeutics and vaccines, even in countries where transmission has not yet been suppressed. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) Figure 2 . COVID-19-related data, estimated effective reproduction number ( ), and its relationship w the average non-residential mobility changes in Jakarta (epidemiological data up to 2 nd September 202 Google Mobility Reports data up to 30 th August 2020). A) Daily reported cases, deaths, and C1 funerals in Jakarta. Black line denotes the daily test positivity ratio; B) Reconstructed daily report cases, deaths, and C19P funerals to reflect the estimated onset date of each observation; C) Colour lines and regions show, respectively, median and 95% CrI of estimated (left axis) based on t reconstructed data (cases, deaths or C19P funerals). Grey areas denote periods where the estimat median is above 1. Black lines and dots denote average changes in non-residential mobility (rig axis); D) The relationship and correlation coefficient between the estimated and the average no residential mobility reduction. with 020; 19P orted ured n the ated (right non- . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . D e d i c a t e d C O V I D -1 9 i s o l a t i o n b e d s a n d I C U b e d s d a t a D a t a f o r t h e c a p a c i t y o f d e d i c a t e d C O V I D -1 9 i s o l a t i o n b e d s a n d I C U b e d s w e r e o b t a i n e d f r o m a r e p o r t f r o m t h e M i n i s t r y o f H e a l t h i n A u g u s t 2 0 2 0 ( 7 ) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . b e g i n n i n g o f t h e e p i d e m i c w a s e s t i m a t e d f o r t h e p e r i o d b e f o r e 2 n d M a r c h 2 0 2 0 ( t h e d a y w h e r e t h e c o u n t r y ' s f i r s t t w o c a s e s w e r e a n n o u n c e d ) . S u b s e q u e n t l y , ܴ ௧ w a s e s t i m a t e d o v e r a w e e k l y s l i d i n g w i n d o w , w i t h a m e a n a n d s t a n d a r d d e v i a t i o n o f s e r i a l i n t e r v a l d i s t r i b u t i o n w e r e a s s u m e d t o b e 6 . 3 a n d 4 . 2 d a y s , r e s p e c t i v e l y ( 1 1 ) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . s t o c h a s t i c d i f f e r e n t i a l e q u a t i o n s r e p r e s e n t i n g a S u s c e p t i b l e -E x p o s e d -I n f e c t e d -R e c o v e r e d ( S E I R ) m o d e l w e r e i m p l e m e n t e d ( o v e r a l l s t r u c t u r e i n F i g u r e S 2 ) . T h e e q u a t i o n s a r e a s f o l l o w . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . r e a c h s e v e r e i n f e c t i o n n e e d i n g h o s p i t a l i s a t i o n ( ‫ܫ‬ ௦ ) , t h e c a s e w a s e i t h e r d e e m e d a c r i t i c a l c a s . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . u p p l e m e n t a r y T a b l e S 2 : L i s t o f d i s t r i c t s , d i s t r i c t s ' i n d e x e s , a n d p r o b a b i l i t y o f d i s e a s e s e v e r i t y a n d o u t c o m e s f o r t h e m e t a p o p u l a t i o n m o d e . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . 9 8 S u m e n e p E a s t J a v a 0 . 0 3 6 0 . 2 1 8 0 . 0 6 6 9 9 K o t a K e d i r i E a s t J a v a 0 . 0 3 3 0 . 2 1 2 0 . 0 6 4 K o t a T a n g e r a n g S e l a t a n B a n t e n 0 . 0 2 7 0 . 1 6 9 0 . 0 4 8 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . u p p l e m e n t a r y T a b l e S 3 : L i s t o f t r a n s m i s s i o n s c e n a r i o s a n d c o u n t e r f a c t u a l s c e n a r i o s f o r m o d e l s i m u l a t i o n . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . R a m a d a n s c e n a r i o 3 N o m o v e m e n t r e d u c t i o n s b e t w e e n d i s t r i c t s d u r i n g t h e R a m a d a n a n d E i d f e s t i v a l s p e r i o d a n d t h e ܴ ௧ v a l u e s d u r i n g t h e p e r i o d w e r e a s s u m e d t o b e t h e s a m e a s e a c h d i s t r i c . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . T h e d a i l y s u s p e c t e d C O V I D -1 9 d e a t h s a r e t h e n d e f i n e d , f o r J a k a r t a , a s t h e d a i l y C 1 9 P f u n e r a l s , a n d f o r f i v e o t h e r J a v a p r o v i n c e s a s t h e m e d i a n o f t h e e s t i m a t e d n u m b e r o f t h e d a i l y s u s p e c t e d C O V I D -1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted October 2, 2020. . R e f e r e n c e s 1 . J a k a r t a P r o v i n c i a l H e a l t h D e p a r t m e n t , J a k a r t a C O V I D -1 9 D a t a M o n i t o r i n g ( 2 0 2 0 ) , ( a v a i l a b l e a t h t t p s : / / c o r o n a . j a k a r t a . g o . i d / i d / d a t a -p e m a n t a u a n ) . 2 . C O V I D -1 9 d i I n d o n e s i a @ k a w a l c o v i d 1 9 o n l i n e s p r e a d s h e e t ( t a b : K a s u s p e r P r o v i n s i ) ( 2 0 2 . S a t u a n T u g a s P e n a n g a n a . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted October 2, 2020. . Novel Coronavirus -China