Abstract
ABSTRACT.Health events emerge from host, community, environment, and pathogen factors—forecasting epidemics is a complex task. We describe an exploratory analysis to identify economic risk factors that could aid epidemic risk assessment. A line list was constructed using the World Health Organization Disease Outbreak News (2016–2018) and economic indicators from the World Bank. Poisson regression employing forward imputations was used to establish relationships with the frequency with which countries reported public health events. Economic indicators demonstrated strong performance appropriate for further assessment in surveillance programming. In our analysis, three economic indicators were significantly associated to event reporting: how much the country’s urban population changed, its average forest area, and a novel economic indicator we developed that assessed how much the gross domestic product changed per capita. Other economic indicators performed less well: changes in total, female, urban, and rural population sizes; population density; net migration; change in per cent forest area; total forest area; and another novel indicator, change in percent of trade as a fraction of the total economy. We then undertook a further analysis of the start of the current COVID-19 pandemic that revealed similar associations, but confounding by global disease burden is likely. Continued development of forecasting approaches capturing information relevant to whole-of-society factors (e.g., economic factors as assessed in our study) could improve the risk management process through earlier hazard identification and inform strategic decision processes in multisectoral strategies to preventing, detecting, and responding to pandemic-threat events.
Highlights
The challenge of employing infectious disease outbreak data usefully to help those managing an emergency and the need to purposefully develop models oriented to decisionmakers and in a context of being connected to management, is increasingly recognized.[1]
We developed and incorporated two novel indicators that we calculated from the World Bank indicators—gross domestic product (GDP) change per capita and trade as a % of GDP change—testing a hypothesis that economic change in either direction is associated with risk as it relates with how people interact within a community with both each other and their environment
We evaluated nine extrinsic factors; four of these were significantly related to frequency of a reported emerging infectious diseases event, with the average forest area square meters being the extrinsic factor with the highest effect in the Poisson analysis
Summary
The challenge of employing infectious disease outbreak data usefully to help those managing an emergency and the need to purposefully develop models oriented to decisionmakers and in a context of being connected to management, is increasingly recognized.[1]. They often are anchored on specific characteristics of how an outbreak pathogen behaves This may result in clumsy applications of information oriented in that way to a community-based perspective of what must be done to prevent and mitigate risks. We attack these challenges from the flank, pursuing in a pilot analysis economic indicators at the population level that may assist prediction in broad strokes across many pathogens, seeking to demonstrate potential utility of openly available information from both health and nonhealth interagencies, and exploring triggers meriting prevention and early mitigation efforts
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