Alarm Variables for Dengue Outbreaks: A Multi-Centre Study in Asia and Latin America

Abstract

BackgroundWorldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently.Methodology/Principal FindingsThe Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007–2013. These data were split between the years 2007–2011 (historic period) and 2012–2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1–12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1–12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4–16 weeks.Conclusions/SignificanceAn increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission.

A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka

“…The findings of the study support the existing evidence on the association between weather variables and dengue incidence [57,58,59,60]. Many studies have identified an overall increased risk of dengue for rainfall and temperature at lag weeks 9–16 [21,61].…”

Modeling Dengue vector population using remotely sensed data and machine learning

“…In particular in Argentina there are interesting experiences on this issue, for example [28,29,30] deal with Dengue epidemics from the dynamic point of view, while [31] are concerned with the deployment of operational tools for its management. At a global scope we can find interesting contributions [32,33,34] with also some operatives experiences [35] Specifically, in a interinstitutional framework between the Argentinean National Space Agency (CONAE) and the Health Ministry of Argentina, there have been initiatives to model the temporal evolution of mosquito populations using environmental variables obtained from remote sensors. These works used series of a few years and are based on a small number of satellite variables [36,37].…”

The role of inter-regional flows in the spread of epidemics in a city of regional influence with a tropical climate

“…En este sentido, es importante que los sistemas de vigilancia epidemiológica y de salud ambiental incorporen estrategias que alcancen a la población que circula entre ciudades con dengue endémico para poder reducir las posibilidades de propagación. Varias prácticas básicas, tales como la piscicultura para reducir el vector y su larva, el uso de repelentes o la notificación veloz a los servicios de salud de síntomas asociados al dengue pueden ayudar a reducir las epidemias, en conjunto con las acciones tradicionales implementadas por agentes sanitarios: el control de basura amontonada, piscinas no cuidadas, tanques de agua sin tapa, floreros y abrevaderos, entre otros sitios favorables para la proliferación de mosquitos (30) .…”