Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines

Abstract

Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention. In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines. The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.048) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.193) and CFR (p = 0.162). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks. Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

“…However, we recognize that realistic estimates of geographic access to healthcare require local knowledge of health-seeking behavior, such as travel modes and speed, as well as information on (seasonal) barriers to mobility. Although we have used local expert knowledge to build accessibility models in previous studies 28 , 30 , 47 , the scale and context of the present analysis did not allow us to use such local knowledge. Such detailed input was beyond the scope of this study, which aims to reflect important differences between population datasets.…”

Consequences of geographical accessibility to post-exposure treatment for rabies and snakebite in Africa: a mini review

“…However, this approach is costly and time-consuming. Correcting under-reporting by considering the distance to care has been recently proposed for addressing the dog bites burden in Madagascar ( 28 ) and the Dengue incidence in the Philippines ( 44 ). To our knowledge, no such correction framework exists for snakebite burden estimation, which could be an area of future research.…”

Towards a leptospirosis early warning system in northeastern Argentina

“…Until recently, development of climate-based EWS for infectious diseases has been mostly done in academic settings. There are many challenges in the implementation of EWS, including lack of historical and real-time data, as well as coarse spatial and temporal resolutions [ 29 ]. Moreover, to translate prediction models and code into usable, automated tools, funding for software development and maintenance is necessary.…”