Jan 2026· Journal of Tropical Medicine· Vol 2026· 0 citations· 27 references
Medicine
TL;DR
Urban hyperendemicity, characterized by a high case volume, coupled with the emerging coastal per‐capita risk, necessitates tailored interventions in Dhaka, while early‐warning surveillance should be implemented in coastal districts.
Abstract
Background Dengue has shifted from Dhaka‐centric to nationwide in Bangladesh. Aim To quantify spatiotemporal patterns and identify high‐risk clusters across all 64 districts from 2021 to 2025, and, extending previous reports with data from 2025, this study aims to observe an ongoing shift in per‐capita risk hotspots. Methods Retrospective ecological study of the Directorate General of Health Services (DGHS) aggregate yearly data on suspected and laboratory‐confirmed dengue admissions (n = 637,626 cases). Incidence per 100,000 population was calculated using 2022 census projections with 1.12% annual growth. Global (Moran’s I) and local (Getis‐Ord Gi∗) spatial autocorrelation and prospective space–time scan statistics (SaTScan) were applied. Results National incidence: 76.4/100,000/year. 2025 rate‐adjusted Moran’s I = 0.328 (p = 0.001). Raw‐case hotspots: Greater Dhaka; population‐adjusted hotspots: Jhalokathi, Barguna (coastal). High‐rate space–time cluster (2024–2025): Dhaka, Manikganj (RR = 3.13, p = 0.001). Dhaka O/E = 4.31 (RR = 6.38). Two low‐rate clusters (north, southeast). Conclusion Urban hyperendemicity, characterized by a high case volume, coupled with the emerging coastal per‐capita risk, necessitates tailored interventions. Intensive vector control is essential in Dhaka, while early‐warning surveillance should be implemented in coastal districts.
Cholera remains a critical public health challenge in Dhaka, Bangladesh, influenced by complex environmental and demographic drivers. This study analyzed around 20 years of 2% surveillance data (2004–2024) from icddr,b (n = 37,361) to investigate long-term trends and climatic associations using distributed lag non-linear models (DLNMs). Climate data (temperature, rainfall, humidity) were obtained from NASA’s Earth Data archives. Findings reveal a significant demographic shift, with adults (>18 years) accounting for 65% of the 6,141 confirmed cholera cases. Seasonality was pronounced; 48% of cases occurred during the pre-monsoon period. While incidence peaked between 2004 and 2008, a recent resurgence was noted from 2019 to 2024. Environmental analysis showed a positive association between temperature and cholera risk, with relative risk (RR) rising from 0.75 at 20°C to 1.10 at 40°C. Precipitation emerged as a major driver, with risk peaking at 150 mm (RR = 1.5), high humidity (90%) also correlated with increased risk (RR = 1.10). The high burden among adults necessitates age-inclusive interventions, including targeted vaccination and WASH measures. Integrating climate data into early warning systems and expanding surveillance to informal settlements are vital for future outbreak prediction and control.
M. Amin, Md. Asif Ahsan, Z. Khan et al.· PLoS Neglected Tropical Dise...· 0 citations
Human brucellosis in Hulunbuir showed persistent seasonality and marked county-level heterogeneity and formal population-adjusted interpretation was strongest for 2016–2024, while 2025 results should be read as provisional reported-case evidence.
Jianglong Jiang, Dan Xu, Xianlin Qiao et al.· Frontiers in Public Health· 0 citations
Purpose: This study aimed to analyze the spatio-temporal patterns of dengue virus infection via kernel density estimation to assess the relative risk distribution and identify transmission hotspots in the Bobonaro municipality.
Methods: A retrospective analysis was conducted on confirmed dengue cases (n=311) reported from seven community health centers and one referral hospital in Bobonaro from January 2022 to December 2024. Kernel density estimation with optimal bandwidth selection was employed to map the relative risk distributions and identify spatial clusters. Demographic patterns across age categories were analyzed using negative binomial regression with a quadratic age-rank term, and sex distribution was analyzed using an exact two-proportion binomial test.
Results: Annual dengue cases increased by 41.48% over the study period, with significant seasonal patterns observed during the dry and rainy seasons. Case counts increased from the infancy category toward a peak in the youth category (5–14 years, 158 cases, 50.8% of all cases) before declining in older age groups, a pattern confirmed by negative binomial regression with a quadratic age-rank term (incidence rate ratio [IRR] 8.45, 95% CI 2.70–25.37, p<0.001 for the linear term and 0.74, 95% CI 0.65–0.85, p<0.001 for the quadratic term). Females accounted for a slightly higher proportion of cases (51.8%, n=161) than males (48.2%, n=150), a difference that was not statistically significant (p= 0.571). Spatial analysis revealed persistent hotspots in southeastern Bobonaro, with new clusters emerging in the northern regions by 2023 and transmission typically contained within an 80 m radius.
Conclusion: This study identified clear demographic vulnerabilities and dynamic spatial patterns of dengue transmission in Bobonaro, demonstrating the utility of GIS-based spatial analysis for strengthening surveillance and guiding targeted vector control in resource-constrained settings.
Zito Viegas da Cruz, I. M. Adnyana· Berita Kedokteran Masyarakat· 0 citations
Leishmaniasis is a vector-borne parasitic disease transmitted by female sandflies of the genus Phlebotomus and affects approximately 1.2 million people annually in more than 90 countries worldwide. Morocco remains one of the most affected countries in North Africa, where the disease continues to represent a major public health concern. This study aimed to analyze the spatial and temporal trends of leishmaniasis incidence across all municipalities of Errachidia Province between 2010 and 2025 and to assess the influence of ecological, demographic, and socio-economic factors on its distribution. Also, the study aimed to predict the monthly CL cases using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Epidemiological data on parasitologically confirmed cases obtained from the Errachidia Provincial Health Delegation were processed using geographic information system (GIS) tools and statistical methods to explore spatial patterns and correlations with environmental and socio-demographic variables. The models are trained and evaluated using monthly CL cases collected from 2010 to 2025, with the optimal model selected based on Akaike Information Criterion (AIC). During 16-years study period, 7034 cases were recorded in Errachidia Province, with the highest incidence reported in 2010 (860 cases per 100,000 inhabitants). Overall, the incidence showed marked temporal fluctuations but demonstrated a general decreasing trend over the study period. A pronounced spatial heterogeneity was observed between rural municipalities (Sid Ali, Melaab, and Ferkla) and urban municipalities (Errachidia, Arfoud, and Goulmima) (p < 0.01). The disease was slightly more frequent in females (54.41%) than in males (45.59%), and a significant difference was observed among age groups (p = 0.019), with the 0-9 and 10-19-year groups being the most affected. Seasonal analysis revealed a peak incidence during winter. In addition, higher incidence was associated with low- to medium-altitude municipalities, while no significant association was observed with poverty or vulnerability indices. The SARIMA (0,0,1)12 model demonstrated the best predictive performance. These findings highlight the heterogeneity and ecological determinants of leishmaniasis in southeastern Morocco and may support targeted surveillance and control strategies in high-risk areas.
Omar El Alaoui, Abdelaati El Khiat, Adnane Hakem et al.· Parasitology international· 0 citations
This study employed geospatial approaches to assess the risk and spatial distribution of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) in Phayao Province, Thailand. Epidemiological data from 2016 to 2024, comprising 3,600 reported cases, were analysed alongside demographic and climatic variables. Temporal analysis revealed a major epidemic in 2023 lasting 20 weeks, coinciding with peak rainfall, with adolescents and young adults (13-24 years old) being the most affected group. Spatial autocorrelation (Moran's I) indicated significant clustering of dengue morbidity rates, with hotspots concentrated in urbanised districts such as Mueang Phayao, Dok Khamtai and Chiang Kham, while Kernel Density Estimation (KDE) highlighted shifts of hotspots toward eastern districts in later years. Local Indicators of Spatial Association (LISA) identified 24 high-high clusters in 2019, predominantly in Mae Chai District. Case-control analysis further revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention. These findings underscore the spatial heterogeneity of dengue transmission and provide geospatial evidence to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.
Phaisarn Jeefoo, Watcharaporn Preedapirom Jeefoo, S. Mekruksavanich et al.· Geospatial Health· 0 citations
ABSTRACT Background: Dengue remains a public health challenge, driven by Aedes aegypti proliferation within complex socio-environmental conditions. Methods: This study investigated the spatiotemporal distribution of dengue in Manaus from 2016 to 2022. Temporal analysis used negative binomial regression adjusted for seasonality. Seasonal and spatial clusters were identified using scan statistics (p < 0.05, 95% CI). A Bayesian spatial model assessed sociodemographic and environmental determinants. Results: Lagged temperature and wind speed showed protective effects. The Bayesian model identified significant associations with population density, vegetation indices (NDVI), sewage infrastructure, and housing types. Conclusions: Integrating climatic and socioeconomic data is essential for intersectoral dengue control strategies.
Mirelia Rodrigues de Araújo, F. Chiaravalloti-Neto, Gerusa Maria Figueiredo· Revista da Sociedade Brasile...· 0 citations
