Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

M. Convertino; A. Reddy; Y. Liu; C. Munoz-Zanzi

doi:10.1016/j.scitotenv.2021.149102

Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

M. Convertino, A. Reddy, Y. Liu, C. Munoz-Zanzi

Environmental Health Sciences

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9 Scopus citations

Abstract

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

Original language	English (US)
Article number	149102
Journal	Science of the Total Environment
Volume	799
DOIs	https://doi.org/10.1016/j.scitotenv.2021.149102
State	Published - Dec 10 2021

Bibliographical note

Funding Information:
M.C. Y.L. and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation, Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570, PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged (http://www.sustainablehealthycities.org). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” (http://www.nimbios.org/workinggroups/WG_leptospira) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University, Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model.

Funding Information:
M.C., Y.L., and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation , Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570 , PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged ( http://www.sustainablehealthycities.org ). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” ( http://www.nimbios.org/workinggroups/WG_leptospira ) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University , Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

Criticality
Endemic
Epidemic
Leptospirosis
Return period
Scaling

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title = "Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts",

abstract = "Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.",

keywords = "Criticality, Endemic, Epidemic, Leptospirosis, Return period, Scaling",

author = "M. Convertino and A. Reddy and Y. Liu and C. Munoz-Zanzi",

note = "Funding Information: M.C. Y.L. and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation, Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570, PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged (http://www.sustainablehealthycities.org). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” (http://www.nimbios.org/workinggroups/WG_leptospira) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University, Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model. Funding Information: M.C., Y.L., and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation , Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570 , PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged ( http://www.sustainablehealthycities.org ). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” ( http://www.nimbios.org/workinggroups/WG_leptospira ) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University , Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

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doi = "10.1016/j.scitotenv.2021.149102",

language = "English (US)",

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AU - Convertino, M.

AU - Reddy, A.

AU - Liu, Y.

AU - Munoz-Zanzi, C.

N1 - Funding Information: M.C. Y.L. and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation, Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570, PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged (http://www.sustainablehealthycities.org). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” (http://www.nimbios.org/workinggroups/WG_leptospira) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University, Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model. Funding Information: M.C., Y.L., and C.M.Z. gratefully acknowledge the funding of the USA National Science Foundation , Ecology of Infectious Diseases award number 0913570 (“Eco-epidemiology of Leptospirosis in Latin America: Understanding the Dynamics of Transmission Within a Community”, http://nsf.gov/awardsearch/showAward?AWD_ID=0913570 , PI: C.M.Z.). Additionally, the NSF SRN funding of the project “Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy, and Livable Cities” is gratefully acknowledged ( http://www.sustainablehealthycities.org ). Y.L. acknowledges the funding of the project “Ecohydrological dynamics governing Leptospirosis epidemiology and optimal portfolio disease management” at the University of Minnesota School of Public Health. M.C. also acknowledges the funding from the Institute for Advanced Study at the University of Minnesota (Resident Fellow Project “HumNat-Health: from People to People. Theory, Computers, Art”). C.M.-Z. and M.C. acknowledge the NIMBioS working group “Mathematical modeling of Leptospira transmission and intervention strategies” ( http://www.nimbios.org/workinggroups/WG_leptospira ) at the University of Tennessee, Knoxville. M.C. also acknowledges the funding from the FY2020 SOUSEI Support Program and Award for Young Researchers (awarded by the Executive Office for Research Strategy to the Top 20% scientists in terms of productivity and citations at Hokkaido University) and the GI-CoRE GSB Station at Hokkaido University , Sapporo, Japan. Cristina Schneider (PAHO WHO) is gratefully acknowledged for providing precious comments on the utility and further implementation of the model. Publisher Copyright: © 2021 Elsevier B.V.

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N2 - Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

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Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

Abstract

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Keywords

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