Vaccination Status and In-hospital Mortality Among Adults With COVID-19 in Jakarta, Indonesia: A Retrospective Hospital-based Cohort Study.

OBJECTIVES: Prospective studies on vaccination status and mortality related to coronavirus disease 2019 (COVID-19) in low-resource settings are still limited. We assessed the association between vaccination status (full, partial, or none) and in-hospital mortality among COVID-19 patients at most hospitals in Jakarta, Indonesia during the Delta predomination wave. METHODS: We conducted a retrospective cohort study among hospitalized COVID-19 patients who met the study criteria (>18 years old and admitted for inpatient treatment because of laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection). We linked individual-level data in the hospital admission database with vaccination records. Several socio-demographic and clinical characteristics were also analyzed. A Cox proportional hazards regression model was used to explore the association between vaccination status and in-hospital mortality in this patient group. RESULTS: In total, 40 827 patients were included in this study. Of these, 70% were unvaccinated (n=28 543) and 19.3% (n=7882) died during hospitalization. The mean age of the patients was 49 years (range, 35-59), 53.2% were female, 22.0% had hypertension, and 14.2% were treated in the intensive care unit, and the median hospital length of stay across the group was 9 days. Our study showed that the risk of in-hospital mortality among fully and partially vaccinated patients was lower than among unvaccinated adults (adjusted hazard ratio [aHR], 0.43; 95% confidence interval [CI], 0.40 to 0.47 and aHR, 0.70; 95% CI, 0.64 to 0.77, respectively). CONCLUSIONS: Vaccinated patients had fewer severe outcomes among hospitalized adults during the Delta wave in Jakarta. These features should be carefully considered by healthcare professionals in treating adults within this patient group.

An investigation of geographical clusters of leptospirosis during the outbreak in Pangandaran, West Java, Indonesia.

Leptospirosis is neglected in many tropical developing countries, including Indonesia. Our research on this zoonotic disease aimed to investigate epidemiological features and spatial clustering of recent leptospirosis outbreaks in Pangandaran, West Java. The study analysed data on leptospirosis notifications between September 2022 and May 2023. Global Moran I and local indicator for spatial association (LISA) were applied. Comparative analysis was performed to characterise the identified hotspots of leptospirosis relative to its neighbourhoods. A total of 172 reported leptospirosis in 40 villages from 9 sub-districts in Pangandaran District were analysed. Of these, 132 cases (76.7%) were male. The median age was 49 years (interquartile range [IQR]: 34-59 years). Severe outcomes including renal failure, lung failure, and hepatic necrosis were reported in up to 5% of the cases. A total of 30 patients died, resulting in the case fatality rate (CFR) of 17.4%. Moran's I analysis showed significant spatial autocorrelation (I=0.293; p=0.002) and LISA results identified 7 High-High clusters (hotspots) in the Southwest, with the total population at risk at 26,184 people. The hotspots had more cases among older individuals (median age: 51, IQR: 36-61 years; p<0.001), more farmers (79%, p=0.001) and more evidence of the presence of rats (p=0.02). A comprehensive One Health intervention should be targeted towards these high-risk areas to control the transmission of leptospirosis. More empirical evidence is needed to understand the role of climate, animals and sociodemographic characteristics on the transmission of leptospirosis in the area studied.

Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence.

Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (Tmean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study.

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID-19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID-19 incidence and related socio-demographic factors explaining the variations of COVID-19 incidence at local level. COVID-19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID-19 dashboard. Global and local spatial clustering of COVID-19 incidence was examined using the Moran's I and local Moran analysis. Socio-demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID-19 incidence was evaluated. The COVID-19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID-19 high-risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20-49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012-1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041-1.050), number of households (IRR = 1.196; 95% CI = 1.193-1.200), access to metered water for washing, and the main occupation of the residents were village level socio-demographic factors associated with the risk of COVID-19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high-risk COVID-19.

Anopheline Diversity in Indonesia: An Evaluation of Animal-Baited Sampling Techniques.

Indonesia has rich Anopheline (Diptera: Culicidae) mosquito species living in various types of ecosystems. The study was conducted to profile and compare Anopheles diversity, equitability, and dominance in various ecosystems using different animal-based sampling techniques. The present study analyzed a subset of data collected from a nation-wide vector and animal reservoirs survey in 2016. Analyses were restricted to three ecosystem types (forest, nonforest, and coastal areas) in Java and Sumatera Islands. A total of 5,477 Anopheles were collected by using animal-baited (n = 1,909) and animal-baited trap nets (n = 1,978), consisting of 23 Anopheline species. Overall, Anopheles vagus was the most abundant species, followed by An. subpictus and An. barbirostris. Among the three ecosystems, the forest had a higher diversity index (H' = 1.98), but each ecosystem has its specific predominant species. Compared with the animal-baited method, the Anopheles abundance collected by animal-baited trap nets was two-fold higher. Ecosystem, elevation, and sampling methods were associated with the abundance of female Anopheles (P-value < 0.001). Our findings revealed that Anopheles were found in a different ecosystem, indicating the potential of malaria transmission. This suggests that improved malaria vector surveillance is essential in all types of ecosystem. Furthermore, the study suggested that animal-baited trap nets could be used as the standard method of outdoor resting sampling in Indonesia in addition to the traditional human landing collection approach.

Extended spectrum beta-lactamase-producing Escherichia coli surveillance in the human, food chain, and environment sectors: Tricycle project (pilot) in Indonesia.

The World Health Organization (WHO) has been implementing antimicrobial surveillance with a "One Health" approach, known as the Global Surveillance ESBL E. coli Tricycle Project. We describe the implementation of the Tricycle Project (pilot) in Indonesia, focusing on its results, challenges and recommendations. The samples were 116 patients with bloodstream infections caused by ESBL E. coli, 100 rectal swabs collected from pregnant women, 240 cecums of broiler, and 119 environmental samples, using the standardized method according to the guidelines. ESBL-producing E. coli was found in 40 (40%) of the 100 pregnant women, while the proportion of ESBL-producing E. coli was 57.7% among the total E. coli-induced bloodstream infections. ESBL-producing E. coli was isolated from 161 (67.1%) out of 240 broilers. On the other hand, the average concentration of E. coli in the water samples was 2.0 × 108 CFU/100 mL, and the ratio of ESBL-producing E. coli was 12.8% of total E. coli. Unfortunately, 56.7% of questionnaires for patients were incomplete. The Tricycle Project (pilot) identified that the proportion of ESBL-producing E. coli was very high in all types of samples, and several challenges and obstacles were encountered during the implementation of the study in Indonesia. The finding of this study have implication to health/the antimicrobial resistance (AMR) surveillance. We recommend continuing this project and extending this study to other provinces to determine the AMR burden as the baseline in planning AMR control strategies in Indonesia. We also recommend improving the protocol of this study to minimize obstacles in the field.

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.

Spatial analysis of malaria in Kotabaru, South Kalimantan, Indonesia: an evaluation to guide elimination strategies.

BACKGROUND: Malaria remains a significant public health concern in Indonesia. Knowledge about spatial patterns of the residual malaria hotspots is critical to help design elimination strategies in Kotabaru district, South Kalimantan, Indonesia. METHODS: Laboratory-confirmed malaria cases from 2012 to 2016 were analysed to examine the trend in malaria cases. Decomposition analysis was performed to assess seasonality. Annual spatial clustering of the incidence and hotspots were identified by Moran's I and the local indicator for spatial association, respectively. RESULTS: The annual parasite incidence of malaria was significantly reduced by 87% from 2012 to 2016. Plasmodium vivax infections were significantly much more prevalent over time, followed by Plasmodium falciparum infections (p<0.001). The monthly seasonality of P. vivax and P. falciparum was distinct. High incidence was spatially clustered identified in the north, west and parts of south Kotabaru. Two persistent and four re-emerging high-risk clusters were identified during the period. Despite the significant reduction in the incidence of malaria, the residual high-risk villages remained clustered in the northern part of Kotabaru. CONCLUSIONS: A spatially explicit decision support system is needed to support surveillance and control programs in the identified high-risk areas to succeed in the elimination goal of 2030.

Original Article: Decline of notified dengue infections in Indonesia in 2017: Discussion of the possible determinants.

This study was conducted to quantify the trend in dengue notifications in the country in 2017 and to explore the possible determinants. Annual nation-wide dengue notification data were obtained from the National Disease Surveillance of Ministry of Health of Indonesia. Annual incidence rate (IR) and case fatality rate (CFR) in 2017 and the previous years were quantified and compared. Correlations between annual larva free index (LFI), implementation coverage of integrated vector management (IVM), El Niño Southern Oscillation (Niño3.4), Dipole Mode Index (DMI), Zika virus seropositivity and the percent change in IR and CFR of dengue were examined. The change of dengue IR and CFRs were mapped. In 2017, dengue IR was declined by 71% (22.55 per 100,000 population) compared to 2016 (77.96 per 100,000 population) while the CFR was slightly reduced from 0.79% to 0.75%. Reduction in IR and CFR occurred in 94.1% and 70.1% out of 34 provinces, respectively. The trend of dengue IR seems to be influenced by Niño3.4 but there is no clear evidence that Niño3.4 is the main reason for dengue reduction in 2017. It is difficult to elucidate that the reduction of dengue in 2017 was associated with previous Zika outbreaks. In conclusion, there was a significant reduction on dengue notifications in Indonesia in 2017. Further investigation is needed to look at the role of climate on the decline of dengue IR at finer temporal scale. In addition, study on the role of cross-protective immunity generated by Zika infection on dengue incidence is also warranted.

TB and HIV Epidemiology and Collaborative Service: Evidence from Ethiopia, 2011-2015.

BACKGROUND: Integrating and scaling up tuberculosis (TB) and HIV services are essential strategies to achieve the combined goals ending both TB and HIV, especially in TB and HIV high burden countries. This study aimed to examine the prevalence of TB and HIV co-infection and the implementation of collaborative services in Ethiopia. METHODS: We used a national sentinel surveillance TB/HIV co-infection collected between 2010 and 2015. The Ethiopian Public Health Institute collected and collated the data quarterly from 79 health facilities in nine regional states and two city administrations. RESULTS: A total of 55,336 people living with HIV/AIDS were screened for active TB between 2011 and 2015. Of these, 7.3% were found co-infected with TB, and 13% TB-negative PLWHA were on isoniazid preventive therapy. Nine out of ten (89.2%) active TB patients were screened for HIV counselling and 17.8% were found to be HIV positive; 78.2% and 53.0% of HIV/TB co-infected patients were receiving cotrimoxazole preventive therapy and antiretroviral treatment, respectively. CONCLUSION: This study showed that the prevalence of TB and HIV co-infection failed to decrease over the study period, and that, while there was an increasing trend for integration of collaborative services, this was not uniform over time. Aligning and integrating TB and HIV responses are still needed to achieve the target of ending TB and HIV by 2030.

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia.

The aim of this study was to assess the possible association of El Niño Southern Oscillation (ENSO) and Dipole Mode Index (DMI) on chikungunya incidence overtime, including the significant reduction in cases that was observed in 2017 in Indonesia. Monthly nation-wide chikungunya case reports were obtained from the Indonesian National Disease Surveillance database, and incidence rates (IR) and case fatality rate (CFR) were calculated. Monthly data of Niño3.4 (indicator used to represent the ENSO) and DMI between 2011 and 2017 were also collected. Correlations between monthly IR and CFR and Niño3.4 and DMI were assessed using Spearman's rank correlation. We found that chikungunya case reports declined from 1972 cases in 2016 to 126 cases in 2017, a 92.6% reduction; the IR reduced from 0.67 to 0.05 cases per 100,000 population. No deaths associated with chikungunya have been recorded since its re-emergence in Indonesia in 2001. There was no significant correlation between monthly Niño3.4 and chikungunya incidence with r = -0.142 (95%CI: -0.320-0.046), p = 0.198. However, there was a significant negative correlation between monthly DMI and chikungunya incidence, r = -0.404 (95%CI: -0.229--0.554) with p < 0.001. In conclusion, our initial data suggests that the climate variable, DMI but not Niño3.4, is likely associated with changes in chikungunya incidence. Therefore, further analysis with a higher resolution of data, using the cross-wavelet coherence approach, may provide more robust evidence.

Variation of preventive practices and its association with malaria infection in eastern Indonesia: Findings from community-based survey.

BACKGROUND: Geographical variation may likely influence the effectiveness of prevention efforts for malaria across Indonesia, in addition to factors at the individual level, household level, and contextual factors. This study aimed to describe preventive practices at individual and a household levels applied by rural communities in five provinces in eastern Indonesia and its association with the incidence of malaria among adult (≥15 years) populations. METHODS: This study analyzed a subset of data of nationally representative community-based survey 2018 Riset Kesehatan Dasar (Riskesdas). Data for socio-demographic (age, gender, education and occupation) and preventive behaviors (use of mosquito bed nets while slept, insecticide-treated mosquito nets (ITNs), mosquito repellent, mosquito electric rackets, mosquito coil/electric anti-mosquito mats, and mosquito window screen) were collected. Data were analyzed using bivariate and multivariable logistic regression model. RESULTS: Total of 56,159 respondents (n = 23,070 households) living in rural areas in Maluku (n = 8044), North Maluku (n = 7356), East Nusa Tenggara (n = 23,254), West Papua (n = 5759) and Papua (n = 11,746) were included in the study. In the multivariable models, using a bed net while slept likely reduced the odds of self-reported malaria among Maluku participants. Reduced odds ratios of self-reported malaria were identified in those participants who used ITNs (North Maluku, ENT, Papua), repellent (Maluku, West Papua, Papua), anti-mosquito racket (ENT), coil (Maluku, North Maluku, Papua) and window screen (West Papua, Papua). CONCLUSION: Our study concluded that the protective effects of preventive practices were varied among localities, suggesting the need for specific intervention programs.

Spatial distribution of leptospirosis incidence in the Upper Yangtze and Pearl River Basin, China: Tools to support intervention and elimination.

BACKGROUND: Since 2011 human leptospirosis incidence in China has remained steadily low with persistent pockets of notifications reported in communities within the Upper Yangtze River Basin (UYRB) and Pearl River Basin (PRB). To help guide health authorities within these residual areas to identify communities where interventions should be targeted, this study quantified the local effect of socioeconomic and environmental factors on the spatial distribution of leptospirosis incidence and developed predictive maps of leptospirosis incidence for UYRB and PRB. METHODS: Data on all human leptospirosis cases reported during 2005-2016 across the UYRB and PRB regions were geolocated at the county-level and included in the analysis. Bayesian conditional autoregressive (CAR) models with zero-inflated Poisson link for leptospirosis incidence were developed after adjustment of environmental and socioeconomic factors such as precipitation, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), land surface temperature (LST), elevation, slope, land cover, crop production, livestock density, gross domestic product and population density. RESULTS: The relationship of environmental and socioeconomic variables with human leptospirosis incidence varied between both regions. While across UYRB incidence of human leptospirosis was associated with MNDWI and elevation, in PRB human leptospirosis incidence was significantly associated with NDVI, livestock density and land cover. Precipitation was significantly and positively associated with the spatial variation of incidence of leptospirosis in both regions. After accounting for the effect of environmental and socioeconomic factors, the predicted distribution of residual high-incidence county is potentially more widespread both in the UYRB and PRB compared to the observed distribution. In the UYRB, the highest predicted incidence was found along the border of Chongqing and Guizhou towards Sichuan basin and northwest Yunnan. The highest predicted incidence was also identified in counties in the central and lower reaches of the PRB. CONCLUSIONS: This study demonstrated significant geographical heterogeneity in leptospirosis incidence within UYRB and PRB, providing an evidence base for prioritising targeted interventions in counties identified with the highest predicted incidence. Furthermore, environmental drivers of leptospirosis incidence were highly specific to each of the regions, emphasizing the importance of localized control measures. The findings also suggested the need to expand interventional coverage and to support surveillance and diagnostic capacity on the predicted high-risk areas.

Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study.

BACKGROUND: Dengue fever control in the tropical island of Bali in Indonesia carries important significance both nationally and globally, as it is one of the most endemic islands in Indonesia and a worldwide popular travel destination. Despite its importance, the spatial and temporal heterogeneity in dengue risk and factors associated with its variation in risk across the island has not been not well explored. This study was aimed to analyze for the first time the geographical and temporal patterns of the incidence of dengue and to quantify the role of environmental and social factors on the spatial heterogeneity of dengue incidence in Bali. METHODS: We analyzed retrospective dengue notification data at the sub-district level (Kecamatan) from January 2012 to December 2017 which obtained from the Indonesian Ministry of Health. Seasonality in notified dengue incidence was assessed by seasonal trend decomposition analysis with Loess (STL) smoothing. Crude standardized morbidity rates (SMRs) of dengue were calculated. Moran's I and local indicators of spatial autocorrelation (LISA) analysis were employed to assess spatial clustering and high-risk areas over the period studied. Bayesian spatial and temporal conditional autoregressive (CAR) modeling was performed to quantify the effects of rainfall, temperature, elevation, and population density on the spatial distribution of risk of dengue in Bali. RESULTS: Strong seasonality of dengue incidence was observed with most cases notified during January to May. Dengue incidence was spatially clustered during the period studied with high-risk kecamatans concentrated in the south of the island, but since 2014, the high-risk areas expanded toward the eastern part of the island. The best-fitted CAR model showed increased dengue risk in kecamatans with high total annual rainfall (relative risk (RR): 1.16 for each 1-mm increase in rainfall; 95% Credible interval (CrI): 1.03-1.31) and high population density (RR: 7.90 per 1000 people/sq.km increase; 95% CrI: 3.01-20.40). The RR of dengue was decreased in kecamatans with higher elevation (RR: 0.73 for each 1-m increase in elevation; 95% CrI: 0.55-0.98). No significant association was observed between dengue RR and year except in 2014, where the dengue RR was significantly lower (RR: 0.53; 95% CrI: 0.30-0.92) relative to 2012. CONCLUSIONS: Dengue incidence was strongly seasonal and spatially clustered in Bali. High-risk areas were spread from kecamatans in Badung and Denpasar toward Karangasem and Klungkung. The spatial heterogeneity of dengue risk across Bali was influenced by rainfall, elevation, and population density. Surveillance and targeted intervention strategies should be prioritized in the high-risk kecamatans identified in this study to better control dengue transmission in this most touristic island in Indonesia. Local health authorities should recommend travelers to use personal protective measures, especially during the peak epidemic period, before visiting Bali.

Climate variability, satellite-derived physical environmental data and human leptospirosis: A retrospective ecological study in China.

BACKGROUND: In the past three decades, the incidence rate of notified leptospirosis cases in China have steeply declined and are now circumscribed to discrete areas in the country. Previous research showed that climate and environmental variation may play an important role in leptospirosis transmission. However, quantitative associations between climate, environmental factors and leptospirosis in the high-risk areas in China, is still poorly understood. OBJECTIVE: To quantify the temporal effects of climate and remotely-sensed physical environmental factors on human leptospirosis in the high-risk counties in China. METHODS: Time series seasonal decomposition was performed to explore the seasonality pattern of leptospirosis incidence in Mengla County, Yunnan and Yilong County, Sichuan for the period 2006-2016. Time series cross-correlation analysis was carried out to examine lagged effects of rainfall, relative humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis. The associations of climatic and physical environment factors with leptospirosis in each county were assessed by using a generalized linear regression model with negative binomial link, adjusted by seasonal components. RESULTS: Leptospirosis incidence in both counties showed strong and unique annual seasonality. Our results show that in Mengla County leptospirosis notifications exhibits a bi-modal temporal pattern while in Yilong County it follows a typical single epidemic curve. After adjusting for seasonality, the final best-fitting model for Mengla County indicated that leptospirosis notifications were significantly associated with present LST values (incidence rate ratio, IRR = 0.857, 95% confidence interval (CI):0.729-0.929) and rainfall at a lag of 6-months (IRR = 0.989; 95% CI: 0.985-0.993). The incidence of leptospirosis in Yilong was associated with rainfall at 1-month lag (IRR = 1.013, 95% CI: 1.003-1.023), LST (3-months lag) (IRR = 1.193, 95% CI: 1.095-1.301), and MNDWI (5-months lag) (IRR = 7.960, 95% CI: 1.241-47.66). CONCLUSIONS: Our study identified lagged effects between leptospirosis incidence and climate and remotely-sensed environmental factors in the two most endemic counties in China. Rainfall in combination with satellite derived physical environment factors provided better insight of the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning systems to support leptospirosis control in China.

Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance.

BACKGROUND: The recent situation of dengue infection in Cirebon district is concerning due to an upsurge trend since the year 2010. The largest dengue outbreak was reported in 2016 which has affected more than 1600 children. A study was conducted to explore the temporal variability of dengue outbreak in Cirebon's child population in during 2011-2017, and to assess the short-term effects of climatic and environmental factor on dengue incidence. In addition, the spatial pattern of dengue incidence in children and high-risk villages were investigated. METHODS: A total of 4597 confirmed dengue cases in children notified from January 2011 to December 2017 were analysed. Seasonal decomposition analysis was carried out to examine the annual seasonality. A generalized linear model (GLM) was applied to assess the short-term effect of climate and normalized difference vegetation index (NDVI) on dengue incidence. The incidence rate ratio (IRR) of the final model was reported. Spatial analyses were conducted by using Moran's I and local indicator of spatial association (LISA) analyses to explore geographical clustering in incidence and to identify high-risk villages for dengue, respectively. RESULTS: An annual dengue epidemic period was observed with peaks occurring every January/February. Based on the GLM, temperature at a lag 4 months (IRR = 1.27; 95% confidence interval, 95% CI: 1.22-1.31, P < 0.001), rainfall at a lag 2 months (IRR = 0.99, 95% CI: 0.99-0.99, P < 0.001), humidity at lag 0 month (IRR = 1.05, 95% CI: 1.04-1.06, P < 0.001) and NDVI at a lag 1 month (IRR = 3.07, 95% CI: 1.94-4.86, P < 0.001) were associated with dengue incidence in children. The dengue incidence in children was spatially varied and clustered at the village level across Cirebon. During 2011-2017, a total of 38 high-risk villages for dengue were identified, which were mainly located in the northern part of Cirebon. CONCLUSIONS: Seasonal patterns of dengue incidence in children in Cirebon were strongly associated with rainfall, temperature, humidity and NDVI variability, suggesting that climatic and environmental data could be used to help predict dengue outbreaks. Our spatial analysis revealed a clustered pattern in dengue incidence and high-risk villages for dengue across Cirebon, suggesting that effective interventions such as vector surveillance and school-based campaigns should be prioritized around the identified high-risk villages. Temporal and spatial analytical tools could be utilized to support local health authorities to apply timely and targeted public health interventions and help better planning and decision-making in order to minimize the impact of dengue outbreaks.

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia.

BACKGROUND: Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far. METHODS: Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach. RESULTS: The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months. CONCLUSIONS: This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.