The epidemic forest reveals the spatial pattern of the spread of acute respiratory infections in Jakarta, Indonesia.

Acute respiratory infection (ARI) is a communicable disease of the respiratory tract that implies impaired breathing. The infection can expand from one to the neighboring areas at a region-scale level through a human mobility network. Specific to this study, we leverage a record of ARI incidences in four periods of outbreaks for 42 regions in Jakarta to study its spatio-temporal spread using the concept of the epidemic forest. This framework generates a forest-like graph representing an explicit spread of disease that takes the onset time, spatio-temporal distance, and case prevalence into account. To support this framework, we use logistic curves to infer the onset time of the outbreak for each region. The result shows that regions with earlier onset dates tend to have a higher burden of cases, leading to the idea that the culprits of the disease spread are those with a high load of cases. To justify this, we generate the epidemic forest for the four periods of ARI outbreaks and identify the implied dominant trees (that with the most children cases). We find that the primary infected city of the dominant tree has a relatively higher burden of cases than other trees. In addition, we can investigate the timely ( R t ) and spatial reproduction number ( R c ) by directly evaluating them from the inferred graphs. We find that R t for dominant trees are significantly higher than non-dominant trees across all periods, with regions in western Jakarta tend to have higher values of R c . Lastly, we provide simulated-implied graphs by suppressing 50% load of cases of the primary infected city in the dominant tree that results in a reduced R c , suggesting a potential target of intervention to depress the overall ARI spread.

Determinant Factors of Mortality in Pre-elderly and Elderly Patients With COVID-19 in Jakarta, Indonesia.

OBJECTIVES: This study aimed to identify risk factors associated with coronavirus disease 2019 (COVID-19) mortality in pre-elderly and elderly individuals in Jakarta, Indonesia. METHODS: We employed a case-control study design, utilizing secondary data from the Epidemiology Surveillance, Immunization Prevention, and Disease Control Sections of the DKI Jakarta Provincial Health Office, collected from December 2020 to January 2021. The study included 188 cases and an equal number of controls. Cases were COVID-19 patients confirmed to have died, as reported by hospitals and communities and subsequently verified by healthcare workers. Control subjects were patients who completed a 14-day isolation period and had been officially declared recovered by healthcare professionals. The dependent variable was the mortality of COVID-19 patients in the January 2021 period. The independent variables consisted of demographic data (age and sex), clinical symptoms (cough, runny nose, anosmia, diarrhea, headaches, abdominal pain, muscle pain, and nausea/vomiting), and comorbidities (hypertension, heart disease, and diabetes). Multivariate analysis was conducted using multiple logistic regression. RESULTS: The multiple logistic regression analysis revealed several factors associated with COVID-19 fatalities in Jakarta: age of 60 years or older (odds ratio [OR], 4.84; 95% CI, 3.00 to 7.80), male (OR, 2.38; 95% CI, 2.41 to 3.68), dyspnea (OR, 3.93; 95% CI, 2.04 to 7.55), anosmia (OR, 0.13; 95% CI, 0.04 to 0.46), and heart disease (OR, 4.38; 95% CI, 1.04 to 18.46). CONCLUSIONS: The control and prevention of COVID-19 among elderly individuals require particular vigilance. When a COVID-19 case is detected within this demographic, prompt treatment and medication administration are crucial to mitigate the presenting symptoms.

The impact of government pandemic policies on the vulnerability of healthcare workers to SARS-CoV-2 infection and mortality in Jakarta Province, Indonesia.

INTRODUCTION: Healthcare workers (HCWs) are on the frontlines of the COVID-19 pandemic, putting them at a higher risk of infection and disease than non-HCWs. We analysed the effects of government policies for the public and for HCWs on the likelihood of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and mortality among HCWs during the first 8 months of the pandemic in Jakarta province, the capital city and COVID-19 hotspot in Indonesia. METHODS: We conducted a retrospective cohort study using secondary data from the Jakarta provincial government from March to October 2020, which included sociodemographic characteristics, symptoms, comorbidities and COVID-19 diagnosis history for all cases. A generalized linear mixed-effect regression model was used to determine the effect of each month on the odds ratio (OR) of COVID-19 cases and deaths for HCW compared with non-HCW, assuming that monthly trends were influenced by varying government policies. RESULTS: A total of 894,487 suspected and confirmed COVID-19 cases in health facilities in Jakarta province were analysed. The OR of confirmed cases for HCW was 2.04 (95% CI 2.00-2.08; p < .001) compared to non-HCW. Despite this higher OR for infection, the case fatality rate (2.32 per 100) and OR (1.02, 95% CI 0.93-1.11; p = .65) of COVID-19 deaths for HCW were similar to those of non-HCW. We observed a trend towards a lower number of COVID-19 patients in hospitals and lower odds of COVID-19 cases among HCWs during the April-to-July 2020 phase compared to the August-to-October phase. This chronologically aligned with more extensive policies to support hospital-based, community-based and well-being-related actions to protect HCW. CONCLUSIONS: HCW had higher odds of having SARS-CoV-2 infection, yet similar odds of death from COVID-19, as compared to non-HCW. Government policies with collective efforts to prevent hospital overcapacity during high transmission and burden periods of the pandemic should be prioritized.

Healthcare workers (HCWs) had higher exposure and odds of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection than non-HCWs but a similar risk of death, consistent with previous studies.Government policies favouring reduced workloads of HCW and interventions to promote resilience can be achieved through combined hospital-based, community-based and well-being-related approaches.Studies to identify the patterns and trends of COVID-19 cases and deaths, hospital admissions and policy dynamics are important to promote evidence-based decision-making by the government.

Pandemic inequity in a megacity: a multilevel analysis of individual, community and healthcare vulnerability risks for COVID-19 mortality in Jakarta, Indonesia.

INTRODUCTION: Worldwide, the 33 recognised megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and healthcare factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning 2 March 2020 to 31 August 2021. METHODS: This retrospective cohort included residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data and disease prevalence from Jakarta Health Office surveillance records, and collected subdistrict level sociodemographics data from various official sources. We used multilevel logistic regression to examine individual, community and subdistrict-level healthcare factors and their associations with COVID-19 mortality. RESULTS: Of 705 503 cases with a definitive outcome by 31 August 2021, 694 706 (98.5%) recovered and 10 797 (1.5%) died. The median age was 36 years (IQR 24-50), 13.2% (93 459) were <18 years and 51.6% were female. The subdistrict level accounted for 1.5% of variance in mortality (p<0.0001). Mortality ranged from 0.9 to 1.8% by subdistrict. Individual-level factors associated with death were older age, male sex, comorbidities and age <5 years during the first wave (adjusted OR (aOR)) 1.56, 95% CI 1.04 to 2.35; reference: age 20-29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1.35, 95% CI 1.17 to 1.55; reference: wealthiest quarter) and high population density (aOR for the highest density 1.34, 95% CI 1.14 to 2.58; reference: the lowest). Healthcare factor associated with death was low vaccine coverage (aOR for the lowest coverage 1.25, 95% CI 1.13 to 1.38; reference: the highest). CONCLUSION: In addition to individual risk factors, living in areas with high poverty and density, and low healthcare performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings.

Seroprevalence of antibodies against SARS-Cov-2 in the high impacted sub-district in Jakarta, Indonesia.

BACKGROUND: Understanding the actual prevalence of COVID-19 transmission in the community is vital for strategic responses to the pandemic. This study aims to estimate the actual infection of COVID-19 through a seroprevalence survey and to predict infection fatality rate (IFR) in Tanjung Priok, the hardest-hit sub-district by the COVID-19 in Jakarta, Indonesia. METHODS: We conducted a venous blood sampling (phlebotomy) to 3,196 individuals in Tanjung Priok between Nov 23, 2020, and Feb 19, 2021 to detect their antibodies against SARS-CoV-2. Using an enumerator-administered questionnaire, we collected data on the respondents' demographic characteristics, COVID-19 test history, COVID-19 symptoms in the last 14 days, comorbidities, and protective behaviours during the last month. We employed descriptive analysis to estimate the seroprevalence and IFR. FINDINGS: The prevalence of Antibody against SARS-CoV-2 was 28.52% (95% CI 25.44-31.81%), with the result being higher in females than males (OR 1.20; 95% CI 1.02-1.42). By the end of the data collection (February 9, 2021), the cumulative cases of COVID-19 in Tanjung Priok were reported to be experienced by 9,861 people (2.4%). Those aged 45-65 were more likely to be seropositive than 15-19 years old (OR 1.42; 95% CI 1.05-1.92). Nearly one third (31%) of the subjects who developed at least one COVID-19 symptom in the last 14 days of the data collection were seropositive. The estimated IFR was 0.08% (95% CI 0.07-0.09), with a higher figure recorded in males (0.09; 95% CI 0.08-0.10) than females (0.07; 95% CI 0.06-0.08), and oldest age group (45-65) (0.21; 95% CI 0.18-0.23) than other younger groups. CONCLUSION: An under-reporting issue was found between the estimated COVID-19 seroprevalence and the reported cumulative cases in Tanjung Priok. More efforts are required to amplify epidemiological surveillance by the provincial and local governments.

Integration of symptomatic, demographical and diet-related comorbidities data with SARS-CoV-2 antibody rapid diagnostic tests during epidemiological surveillance: a cross-sectional study in Jakarta, Indonesia.

OBJECTIVES: Affordable options for COVID-19 epidemiological surveillance are needed. Virus detection by reverse transcription-PCR (RT-PCR) is sensitive but costly, and antigen-based rapid diagnostic tests (RDTs) are cheap but with reduced sensitivity; both detect current infection but not exposure. RDT-IgM/IgG antibodies to SARS-CoV-2 detect exposure but have poor sensitivity for current infection. We investigated if the integration of symptomatic, demographical and diet-related comorbidities data with antibody RDTs improves their potential to assess infection rates in addition to exposure, thereby broadening their utility for surveillance. DESIGN: We conducted a cross-sectional study using data from community surveillance for SARS-CoV-2. Health workers collected nasopharyngeal swabs for RT-PCR and RDT antigen assessments and venous blood for RDT-IgM/IgG from symptomatic and asymptomatic persons. Data on age, gender, contact history, symptoms (ie, fever, cough, runny nose, sore throat, headache, dyspnoea and diarrhoea), diet-related comorbidities (ie, diabetes and hypertension) and chest radiology were collected. SETTING: High-risk communities in Jakarta, Indonesia, in May 2020. PARTICIPANTS: 343 community members' data were included. OUTCOME MEASURES: RDT-IgM/IgG sensitivity, specificity and predictive values and area under receiver operating characteristic curve for RT-PCR positivity using RDT results alone and in combination with other predictors, including symptom components derived from principal component analysis. RESULTS: There were 24 PCR-confirmed infections. RDT-IgM/IgG-positive tests were associated with infection (OR 10.8, 95% CI 4.43 to 26.4, p<0.001) with an area under the curve (AUC) of 0.708% and 50% sensitivity, 91.5% specificity, 30.8% positive predictive value (PPV) and 96.1% negative predictive value (NPV). RDT results combined with age, gender, contact history, symptoms and comorbidities increased the AUC to 0.787 and yielded 62.5% sensitivity, 87.0% specificity, 26.6% PPV and 96.9% NPV. CONCLUSIONS: SARS-CoV-2 RDT-IgM/IgG results integrated with other predictors may be an affordable tool for epidemiological surveillance for population-based COVID-19 exposure and current infection, especially in groups with outbreaks or high transmission.

Using syndromic measures of mortality to capture the dynamics of COVID-19 in Java, Indonesia, in the context of vaccination rollout.

BACKGROUND: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island. METHODS: We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine rollout. RESULTS: C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign. CONCLUSIONS: Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.

Clinical characteristics and mortality associated with COVID-19 in Jakarta, Indonesia: A hospital-based retrospective cohort study.

BACKGROUND: Data on COVID-19-related mortality and associated factors from low-resource settings are scarce. This study examined clinical characteristics and factors associated with in-hospital mortality of COVID-19 patients in Jakarta, Indonesia, from March 2 to July 31, 2020. METHODS: This retrospective cohort included all hospitalised patients with PCR-confirmed COVID-19 in 55 hospitals. We extracted demographic and clinical data, including hospital outcomes (discharge or death). We used logistic regression to examine factors associated with mortality. FINDINGS: Of 4265 patients with a definitive outcome by July 31, 3768 (88%) were discharged and 497 (12%) died. The median age was 46 years (IQR 32-57), 5% were children, and 31% had >1 comorbidity. Age-specific mortalities were 11% (7/61) for <5 years; 4% (1/23) for 5-9; 2% (3/133) for 10-19; 2% (8/638) for 20-29; 3% (26/755) for 30-39; 7% (61/819) for 40-49; 17% (155/941) for 50-59; 22% (132/611) for 60-69; and 34% (96/284) for ≥70. Risk of death was associated with higher age, male sex; pre-existing hypertension, diabetes, or chronic kidney disease; clinical diagnosis of pneumonia; multiple (>3) symptoms; immediate ICU admission, or intubation. Across all ages, risk of death was higher for patients with >1 comorbidity compared to those without; notably the risk was six-fold increased among patients <50 years (adjusted odds ratio 5.87, 95%CI 3.28-10.52; 27% vs 3% mortality). INTERPRETATION: Overall in-hospital mortality was lower than reported in high-income countries, probably due to younger age distribution and fewer comorbidities. Deaths occurred across all ages, with >10% mortality among children <5 years and adults >50 years.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia.

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Factors Associated with Death in COVID-19 Patients in Jakarta, Indonesia: An Epidemiological Study.

BACKGROUND: Coronavirus Disease 2019 is an emerging respiratory disease that is now a pandemic. Indonesia is experiencing a rapid surge of cases but the local data are scarce. METHODS: this is an analysis using data from the ongoing recapitulation of Epidemiological Surveillance (ES) by the Provincial Health Office of Jakarta from March 2nd to April 27th 2020. We evaluated demographic and clinical characteristics of all confirmed cases in association with death. RESULTS: of the 4,052 patients, 381 (9.4%) patients were deceased. Multivariable analysis showed that death was associated with older age (odds ratio [OR] 1.03; 95% confidence interval [CI] 1.02, 1.05, per year increase; p<0.001), dyspnea (OR 4.83; 95% CI 3.20, 7.29; p<0.001), pneumonia (OR 2.46; 95%CI 1.56, 3.88; p<0.001), and pre-existing hypertension (OR 1.86; 95% CI 1.24, 2.78; p=0.003). Death was highest in the week of April 6th 2020 and declined in the subsequent weeks, after a large-scale social restriction commenced. CONCLUSION: older age, dyspnea, pneumonia, and pre-existing hypertension were associated with death. Mortality was high, but may be reduced by lockdown.