A global dataset of publicly available dengue case count data.

OpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.

Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings.

Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014-16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.

Travel time to health facilities in areas of outbreak potential: maps for guiding local preparedness and response.

BACKGROUND: Repeated outbreaks of emerging pathogens underscore the need for preparedness plans to prevent, detect, and respond. As countries develop and improve National Action Plans for Health Security, addressing subnational variation in preparedness is increasingly important. One facet of preparedness and mitigating disease transmission is health facility accessibility, linking infected persons with health systems and vice versa. Where potential patients can access care, local facilities must ensure they can appropriately diagnose, treat, and contain disease spread to prevent secondary transmission; where patients cannot readily access facilities, alternate plans must be developed. Here, we use travel time to link facilities and populations at risk of viral hemorrhagic fevers (VHFs) and identify spatial variation in these respective preparedness demands. METHODS AND FINDINGS: We used geospatial resources of travel friction, pathogen environmental suitability, and health facilities to determine facility accessibility of any at-risk location within a country. We considered in-country and cross-border movements of exposed populations and highlighted vulnerable populations where current facilities are inaccessible and new infrastructure would reduce travel times. We developed profiles for 43 African countries. Resulting maps demonstrate gaps in health facility accessibility and highlight facilities closest to areas at risk for VHF spillover. For instance, in the Central African Republic, we identified travel times of over 24 h to access a health facility. Some countries had more uniformly short travel times, such as Nigeria, although regional disparities exist. For some populations, including many in Botswana, access to areas at risk for VHF nationally was low but proximity to suitable spillover areas in bordering countries was high. Additional analyses provide insights for considering future resource allocation. We provide a contemporary use case for these analyses for the ongoing Ebola outbreak. CONCLUSIONS: These maps demonstrate the use of geospatial analytics for subnational preparedness, identifying facilities close to at-risk populations for prioritizing readiness to detect, treat, and respond to cases and highlighting where gaps in health facility accessibility exist. We identified cross-border threats for VHF exposure and demonstrate an opportunity to improve preparedness activities through the use of precision public health methods and data-driven insights for resource allocation as part of a country's preparedness plans.

Global distribution and environmental suitability for chikungunya virus, 1952 to 2015.

Chikungunya fever is an acute febrile illness caused by the chikungunya virus (CHIKV), which is transmitted to humans by Aedes mosquitoes. Although chikungunya fever is rarely fatal, patients can experience debilitating symptoms that last from months to years. Here we comprehensively assess the global distribution of chikungunya and produce high-resolution maps, using an established modelling framework that combines a comprehensive occurrence database with bespoke environmental correlates, including up-to-date Aedes distribution maps. This enables estimation of the current total population-at-risk of CHIKV transmission and identification of areas where the virus may spread to in the future. We identified 94 countries with good evidence for current CHIKV presence and a set of countries in the New and Old World with potential for future CHIKV establishment, demonstrated by high environmental suitability for transmission and in some cases previous sporadic reports. Aedes aegypti presence was identified as one of the major contributing factors to CHIKV transmission but significant geographical heterogeneity exists. We estimated 1.3 billion people are living in areas at-risk of CHIKV transmission. These maps provide a baseline for identifying areas where prevention and control efforts should be prioritised and can be used to guide estimation of the global burden of CHIKV.