Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Abstract

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

“…To improve surveillance and monitor of dengue occurrences and Aedes mosquitoes, intercomparison model projects could help to identify the most general and efficient models considering various geographical contexts and data set: ( [94], e.g., Airborne spread of foot-and-mouth disease -Model intercomparison; https://www.theia-land.fr/en/anisette-tracking-mosquitoes-thatcarry-disease/, e.g., Inter-Site Analysis: Evaluation of Remote Sensing as a predictive tool for the surveillance and control of diseases caused by mosquito, and future impacts of climate and/or land use changes may also be considered; [95], e.g., Malaria and climate; [17,23,96], e.g., Urbanization). Review of literature are also needed to update the ever-increasing output of scientific publications, and lead to new synthetic insights ( [97]; [10], e.g., Determinants of Aedes Mosquito Habitat for Risk Mapping, [98], e.g., New frontiers for environmental epidemiology in a changing world, [99], e.g., Current challenges for dengue; [100], e.g., Mosquito-Borne Diseases: Advances in Modelling Climate-Change Impacts; [101], e.g., A 10 years view of scientific literature on Aedes aegypti; [102], e.g., Satellite Earth Observation Data in Epidemiological Modeling).…”

“…• although we did not consider meteorological factors here, surface air temperature or soil moisture, traditionally measured by in situ weather stations, could be derived from satellite passive microwave radiometry [102,125].…”

Satellitendaten zur Erfassung gesundheitsrelevanter Umweltbedingungen: Beispiele und interdisziplinäre Potenziale

“…Hierzu zählen auch Daten zu Umweltbedingungen, die die menschliche Gesundheit je nach Ausprägung positiv oder negativ beeinflussen können. Beispiele dafür sind Luftschadstoffbelastungen [3], verschmutztes Trinkwasser [4], Lärm [5], Wetterverhältnisse [6], UV-Strahlung [7], Verfügbarkeit von Frei-und Erholungsflächen [8], Umweltgifte [9], Lichtverschmutzung [10], Umweltbedingungen, die die Übertragung von Krankheiten wie Malaria, Cholera oder Denguefieber begünstigen [11], oder auch Innenraumbelastungen [12]. Zwischen Umweltbedingungen und der Gesundheit finden dabei auf unterschiedlichsten räumlichen Skalen komplexe Wechselwirkungen statt.…”

Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

“…While such indicators may be imperfect, they can provide information for local conditions in the absence of other granular statistics collected on the ground. Targeted studies at relatively small spatial scales have illustrated the usefulness of proxy data such as weather/climate [6][7][8] , demographics 9 , multispectral satellite imagery 10,11 , and social internet data 12,13 to enhance predictions. However, the field is limited by a lack of consistent metrics, the need for broad comparative assessment of data streams, and the need for methods that can work across the complex matrix of eco-regions and human systems where mosquito-borne diseases are endemic.…”

“…Windows of prediction include nowcasting (necessary since reported case counts often have a lag of 1-4 weeks) 19 , forecasting weekly cases 4-12 weeks into the future 20 , and several-months ahead categorical risk forecasting 21 . Many of these studies have exploited multiple data streams including socioeconomic drivers 9 , weather data 6 , satellite imagery 11 , topography (e.g., altitude), entomological factors 24 , Internet data (e.g., social media) 13,25 , and clinical surveillance data 19 . However, most of these studies have not evaluated the contribution of each data stream on the forecast, with a few exceptions that have evaluated the contribution of one data source 12 or a single type of data stream 20 .…”