Identifying Hotspots in the Distribution of Human Infectious Diseases Using a Bayesian Framework: A Lead to Drivers, Prevention, and Surveillance of Disease Emergence

Abstract Background The ongoing COVID-19 pandemic underscores the need of surveillance system to detect threats and regions at high risk from emerging infectious diseases (EIDs). With the human-driven perturbations to the human-animal-pathogen interface at an ecological scale, the integration of these environmental drivers is essential. We propose robust mathematical models to map, detect, and identify significant drivers of EID outbreaks for three viral EID groups: Filoviridae, Coronaviridae, and Henipaviruses.MethodsWe modeled the presence-absence data in a spatially explicit, binomial and zero-inflation binomial (ZIB) logistic regression with and without autoregression (iCAR). The presence data were extracted from WHO and Promed archives for the three EID groups and we generated pseudoabsence points within the spatial distribution of the mammalian reservoirs. Various environmental and demographical raster were used to explain the distribution of EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models.ResultsWe used hierarchical SDM binomial, ZIB with and without iCAR models to map the predictive risk of viral EIDs. ZIB models with autoregression were found to be near perfect in detecting the distribution of EID outbreaks with 70% of the models explained by environmental and demographic drivers. The common influencing drivers amongst the three groups of EIDs analyzed were climatic covariates minimum temperature and rainfall, and human-driven land modifications.ConclusionsOur study results conclude that using SDMs in a Bayesian structure is near perfect detecting hotspots and significant drivers of EID outbreaks. It also maps the sites needing active surveillance, which essential in epidemic prevention, and highlights the influence of human-driven modifications to environment on disease emergence.

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Human-Altered Landscapes and Climate To Predict Human Infectious Disease Hotspots

10.21203/rs.3.rs-1104482/v1 ◽

2021 ◽

Author(s):

Soushieta Jagadesh ◽

Marine Combe ◽

Mathieu Nacher ◽

Rodolphe Elie Gozlan

Keyword(s):

High Risk ◽

Emerging Infectious Diseases ◽

Disease Outbreaks ◽

Viral Disease ◽

Environmental Drivers ◽

Surveillance Systems ◽

Disease Emergence ◽

Presence Data ◽

Risk Areas ◽

Risk Of Disease

Abstract Background Zoonotic diseases account for more than 70% of emerging infectious diseases. Due to their increasing incidence, and impact on global health and economy, anticipating the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. Methods We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regression with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. Results For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of disease reservoirs and hosts, as well as data on the distribution of each disease. Common influencing drivers are climatic covariates (minimum temperature and rainfall) and human-induced land modifications. Conclusions Using topographical, climatic and previous disease outbreaks reports, we show that we can identify and predict future high-risk areas for disease emergence, such as the current COVID-19 pandemic, and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.

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Emerging Infectious Diseases

EDIS ◽

10.32473/edis-in722-2007 ◽

2007 ◽

Vol 2007 (16) ◽

Author(s):

Jorge R. Rey

Keyword(s):

Infectious Diseases ◽

Zika Virus ◽

Emerging Infectious Diseases ◽

Fact Sheet ◽

New Information ◽

Common Causes ◽

The Common

ENY-740, a 10-page illustrated fact sheet by Jorge R. Rey, describes what emerging infectious diseases are, gives some examples, some of the common causes for emergence or re-emergence of the diseases. Includes references. Published by the UF Department of Entomology and Nematology, May 2007. Updated in February 2016 to include new information on Chikungunya and the Zika virus. Retired from the EDIS website 7/25/2019.

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In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO’s Blueprint Priority Diseases

10.1101/2020.03.27.20044156 ◽

2020 ◽

Author(s):

Soushieta Jagadesh ◽

Marine Combe ◽

Mathieu Nacher ◽

Rodolphe Elie Gozlan

Keyword(s):

Emerging Infectious Diseases ◽

Species Distribution Modelling ◽

Environmental Data ◽

Tree Cover ◽

Support Vector ◽

Disease Emergence ◽

Natural Habitats ◽

Valley Fever ◽

Risk Of Disease ◽

Predictive Risk

AbstractAnthropization of natural habitats including climate change along with overpopulation and global travel have been contributing to emerging infectious diseases outbreaks. The recent COVID-19 outbreak in Wuhan, highlights such threats to human health, social stability and global trade and economy. We used species distribution modelling and environmental data from satellite imagery to model Blueprint Priority Diseases occurrences. We constructed classical regression and Support Vector Machine models based on environmental predictor variables such as landscape, tree cover loss, climatic covariates. Models were evaluated and a weighed mean was used to map the predictive risk of disease emergence. We mapped the predictive risk for filovirus, Nipah, Rift Valley Fever and coronavirus diseases. Elevation, tree cover loss and climatic covariates were found to significant factors influencing disease emergence. We also showed the relevance of disease biogeography and in the identification potential hotspots for Disease X in regions in Uganda and China.Article Summary LineIn our study with the use of a biogeographic approach, we were able to identify Wuhan as a potential hotspot of disease emergence in the absence of COVID-19 data and we confirm that distribution of disease emergence in humans is spatially dependent on environmental factors.

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Global Change and Emerging Infectious Diseases

10.22541/au.163578736.62541507/v1 ◽

2021 ◽

Author(s):

Nicole Nova ◽

Tejas S Athni ◽

Marissa L Childs ◽

Lisa Mandle ◽

Erin A Mordecai

Keyword(s):

Global Change ◽

Infectious Diseases ◽

Disease Ecology ◽

Interdisciplinary Collaboration ◽

Emerging Infectious Diseases ◽

Economic Incentives ◽

Disease Emergence ◽

Global Movement ◽

Almost All ◽

Life On Earth

Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change-including climate change, land-use change, urbanization, and global movement of individuals, species, and goods-may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability, and human and planetary health.

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Enfermedades Infecciosas Emergentes

EDIS ◽

10.32473/edis-in731-2007 ◽

2007 ◽

Vol 2007 (19) ◽

Author(s):

Jorge R. Rey

Keyword(s):

Infectious Diseases ◽

Emerging Infectious Diseases ◽

Spanish Language ◽

Language Version ◽

Fact Sheet ◽

Common Causes ◽

The Common

ENY-740S, a 5-page illustrated fact sheet by Jorge R. Rey, is the Spanish language version of ENY-740, "Emerging Infectious Diseases." It describes what emerging infectious diseases are, gives some examples, some of the common causes for emergence or re-emergence of the diseases. Includes references. Published by the UF Department of Entomology and Nematology, September 2007.

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Emerging infectious diseases of amphibians in Poland: distribution and environmental drivers

Diseases of Aquatic Organisms ◽

10.3354/dao03631 ◽

2021 ◽

Author(s):

G Palomar ◽

J Jakóbik ◽

J Bosch ◽

K Kolenda ◽

M Kaczmarski ◽

...

Keyword(s):

Infectious Diseases ◽

Emerging Infectious Diseases ◽

Environmental Drivers

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Emerging human infectious diseases of aquatic origin: a comparative biogeographic approach using Bayesian spatial modelling

International Journal of Health Geographics ◽

10.1186/s12942-019-0188-6 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Soushieta Jagadesh ◽

Marine Combe ◽

Pierre Couppié ◽

Paul Le Turnier ◽

Loïc Epelboin ◽

...

Keyword(s):

Infectious Diseases ◽

Spatial Structure ◽

Environmental Changes ◽

Emerging Infectious Diseases ◽

Buruli Ulcer ◽

Disease Outbreaks ◽

Emerging Diseases ◽

Bayesian Regression ◽

Environmental Drivers ◽

Environmental Predictors

Abstract Background With the increase in unprecedented and unpredictable disease outbreaks due to human-driven environmental changes in recent years, we need new analytical tools to map and predict the spatial distribution of emerging infectious diseases and identify the biogeographic drivers underpinning their emergence. The aim of the study was to identify and compare the local and global biogeographic predictors such as landscape and climate that determine the spatial structure of leptospirosis and Buruli Ulcer (BU). Methods We obtained 232 hospital-confirmed leptospirosis (2007–2017) cases and 236 BU cases (1969–2017) in French Guiana. We performed non-spatial and spatial Bayesian regression modeling with landscape and climate predictor variables to characterize the spatial structure and the environmental drivers influencing the distribution of the two diseases. Results Our results show that the distribution of both diseases is spatially dependent on environmental predictors such as elevation, topological wetness index, proximity to cropland and increasing minimum temperature at the month of potential infection. However, the spatial structure of the two diseases caused by bacterial pathogens occupying similar aquatic niche was different. Leptospirosis was widely distributed across the territory while BU was restricted to the coastal riverbeds. Conclusions Our study shows that a biogeographic approach is an effective tool to identify, compare and predict the geographic distribution of emerging diseases at an ecological scale which are spatially dependent to environmental factors such as topography, land cover and climate.

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Pandemic inequalities: emerging infectious diseases and health equity

International Journal for Equity in Health ◽

10.1186/s12939-021-01611-2 ◽

2022 ◽

Vol 21 (1) ◽

Author(s):

Clare Bambra

Keyword(s):

Infectious Diseases ◽

Health Equity ◽

Emerging Infectious Diseases ◽

Disease Emergence ◽

Research Directions ◽

Unequal Treatment ◽

Considerable Research ◽

Transmission Case ◽

The Relationship ◽

Economic Inequalities

AbstractThe frequency and scale of Emerging Infectious Diseases (EIDs) with pandemic potential has been increasing over the last two decades and, as COVID-19 has shown, such zoonotic spill-over events are an increasing threat to public health globally. There has been considerable research into EIDs – especially in the case of COVID-19. However, most of this has focused on disease emergence, symptom identification, chains of transmission, case prevalence and mortality as well as prevention and treatment. Much less attention has been paid to health equity concerns and the relationship between socio-economic inequalities and the spread, scale and resolution of EID pandemics. This commentary article therefore explores socio-economic inequalities in the nature of EID pandemics. Drawing on three diverse case studies (Zika, Ebola, COVID-19), it hypothesises the four main pathways linking inequality and infectious disease (unequal exposure, unequal transmission, unequal susceptibility, unequal treatment) – setting out a new model for understanding EIDs and health inequalities. It concludes by considering the research directions and policy actions needed to reduce inequalities in future EID outbreaks.

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