Abstract
A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases.
Highlights
Climate warming trends have been accelerating over the last few decades
To address the need for closer to real-time surveillance of emerging issues and earlier insight on potential health impacts, two risk assessment strategies have been, and are being, developed: event-based surveillance (EBS) systems, which increasingly incorporate artificial intelligence; and risk modelling. The objective of this overview is to describe these two risk assessment strategies and how they can inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases
The Zika virus is estimated to have first appeared in Brazil between August 2013 and April 2014 by infected travellers entering the country at Rio de Janeiro, Brasilia, Fortaleza and/ or Salvador; and this introduction was followed by epidemics in Haiti, Honduras, Venezuela and Colombia [21]
Summary
Climate warming trends have been accelerating over the last few decades. The world’s nine warmest years in the time period from 1850 to 2017 have all occurred in the last twelve years, with a total increase of approximately 0.97°C in the average annual air temperature for the time period from 1880 to 2017 [1]. To address the need for closer to real-time surveillance of emerging issues and earlier insight on potential health impacts, two risk assessment strategies have been, and are being, developed: event-based surveillance (EBS) systems, which increasingly incorporate artificial intelligence; and risk modelling The objective of this overview is to describe these two risk assessment strategies and how they can inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases. In China, the expected number of cases of hand, foot and mouth disease in children was best predicted by including data on weekly temperature and precipitation as well as data on hand, foot and mouth disease-related queries from the Chinese Baidu search engine [61] Another dominant risk modelling approach is the use of compartmental models to mathematically simulate transmission dynamics of a population; that is, the flow of individuals among health states, such as susceptible (S), infectious (I) and recovered (R). The Zika virus is estimated to have first appeared in Brazil between August 2013 and April 2014 by infected travellers entering the country at Rio de Janeiro, Brasilia, Fortaleza and/ or Salvador; and this introduction was followed by epidemics in Haiti, Honduras, Venezuela and Colombia [21]
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