Objective:We analyzed the epidemiological trends and spatio-temporal distribution characteristics of varicella incidence in Baise City from 2010 to 2024 to provide a basis for its prevention and control. Methods:This study describes the epidemiological characteristics of varicella from population, temporal and spatial perspectives by using data from the National Infectious Disease Reporting System. Trend and spatial autocorrelation analyses and spatiotemporal scanning statistics were employed to determine the spatial clustering and spatiotemporal dynamics for the incidence of varicella. Results:About 54,969 cases of varicella were reported during the study period, with an annual incidence rate of 102.30 per 100,000, and an annual percentage change of 10.05% (95% CI: 1.10% to 19.79%; P = 0.027). The male-to-female ratio was 1.23:1, with the peak incidence occurring among in the 5-9 age group and the 10-19 age group showed an upward trend in incidence. People under 20 years old had the highest incidence rates of varicella and students accounted for more than half of the cases (55.46%), and 45.19% of townships had an average annual incidence rate exceeding the city's average level. There was a Positive spatial autocorrelation was observed in the varicella incidence across different townships, with a pattern of high-value clustering. Local spatial autocorrelation analysis identified a total of 117 hot spots. Spatiotemporal scanning identified five significant clusters, these included a Most likely cluster of 27 towns in the Youjiang River Valley and secondary clusters in the northwestern mountainous regions of Baise. Conclusion:The reported incidence rate of varicella in Baise was on the rise, exhibiting distinct spatio-temporal clustering characteristics. Hotspots were aligned with the spatio-temporal clustering zones. Strengthening prevention and control measures in local areas where clusters and hotspots of varicella occur can be an effective strategy to decrease its incidence in a city.
Low birth weight is a major contributor to population ill health. Here, using data collected between January 2021 and August 2024, we performed an interrupted time-series analysis to assess the effect of a government-led cash-plus intervention in India on maternal weight and birth weight. The intervention targeted parity-two births and combined cash transfers and social behaviour-change communication to improve nutrition. Birth weight and third-trimester pregnancy weight was available for 90,732 newborns and 60,899 of their mothers. We observed an absolute reduction in the prevalence of low birth weight of 7.4% (95% confidence interval (CI) 5.3–9.6) and an increase in mean third-trimester pregnancy weight of 3.4 kg (95% CI 3.0–3.8). This cash-plus intervention, implemented within existing government systems in a setting with a high burden of maternal and newborn undernutrition, was associated with improved pregnancy weight gain and a reduction in the prevalence of low birth weight. An interrupted time-series analysis of routine data from 2021 to 2024 shows that a government-led cash-plus programme for parity-two pregnancies in India, evaluated among 90,732 births and 60,899 mothers, was associated with a 7.4% absolute reduction in low birth weight and a 3.4-kg increase in third-trimester maternal weight.
Dengue fever remains a major public health concern in the northern region of Thailand, where periodic outbreaks impose substantial health and economic burdens. Understanding spatial and temporal patterns of dengue incidence rate is essential for improving surveillance, forecasting, and targeted control strategies. This study examined the spatial and temporal dynamics of dengue incidence rate across eight provinces in the northern region of Thailand and developed predictive models to support early warning and prevention efforts. Monthly dengue case data from 2012 to 2024 were obtained from the national surveillance system and aggregated at the provincial level. Spatial analyses included the calculation of incidence rates, spatial empirical Bayesian (SEB) smoothing, and assessment of spatial clustering using Moran’s I and local indicators of spatial association (LISA). Temporal analyses employed seasonal-trend decomposition and seasonal autoregressive integrated moving average (SARIMA) modeling to forecast dengue incidence rate at regional and provincial levels. To ensure normal transmission patterns, incidence rates during the COVID-19 period were excluded. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). Dengue incidence rate showed clear seasonality, with annual peaks during the rainy season (June–August). Spatial heterogeneity was evident, with Chiang Mai and Mae Hong Son consistently exhibiting high incidence rate and significant spatial clustering. The SARIMA (2,0,0)(2,1,0)12 model accurately captured regional dengue dynamics, yielding MAE, RMSE, and MAPE of 2.93, 3.30, and 31.86
Numerous trials have evaluated the effectiveness of mass drug administration (MDA) in rapidly reducing malaria transmission, but it is unknown whether the estimated effects generalize to other populations eligible for MDA. A recent cluster randomized trial in Senegal found that MDA reduced malaria incidence by 55% in areas that routinely deploy seasonal malaria chemoprevention (SMC). Here, we used transportability models with machine learning to generalize trial effects to 116 non-trial communes where SMC is the standard of care. Accounting for differences in weather, vegetation and population density between trial and non-trial areas, we estimated considerable reductions in incidence (ranging from 36% to 65%) in 74 non-trial communes, with larger decreases in areas having higher precipitation, denser vegetation and lower temperatures. We found that MDA was not effective in the postintervention year in non-trial communes, supporting the notion that MDA's effects are short-lived. Our approach offers a scalable framework for generalizing trial findings to target environmentally mediated infectious disease interventions.
As insecticide resistance spreads in Africa, next-generation insecticide-treated nets (ITNs) are increasingly being deployed to protect vulnerable populations against malaria. While these nets provide greater entomological efficacy against resistant mosquitoes, their effectiveness against malaria transmission also depends on other factors, such as durability, access, usage, and activity patterns of hosts and vectors. Here, we quantify the impact of two next-generation ITNs, namely Interceptor®G2 (chlorfenapyr-pyrethroid) and Olyset® Plus (piperonyl butoxide-pyrethroid), in a cascade from entomological efficacy to population-level effectiveness. We use a mathematical model that we parameterize with entomological data and validate against results from randomized controlled trials. We found that, beyond entomological factors, operational factors including functional survival, ITN use and in-bed exposure critically impact ITN effectiveness overall and per ITN types. Our results obtained for Tanzania can be extended to other contexts in a dashboard allowing users to explore product selection based on setting-specific factors that influence ITN effectiveness.
