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

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.

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

2021 ◽  
Author(s):  
Soushieta Jagadesh ◽  
Marine Combe ◽  
Mathieu Nacher ◽  
Rodolphe Elie Gozlan
Keyword(s):  
Infectious Diseases ◽  
Emerging Infectious Diseases ◽  
Environmental Drivers ◽  
Spatially Explicit ◽  
Disease Emergence ◽  
Ecological Scale ◽  
Presence Data ◽  
Study Results ◽  
The Common ◽  
Predictive Risk

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.

scholarly journals Spatial-time Visualization of Cutaneous Leishmaniasis in an Urban Area: Informing Health Policy and Practice

2020 ◽  
Author(s):  
Neda Firouraghi ◽  
Sayyed Mostafa Mostafavi ◽  
Amene Raouf-Rahmati ◽  
Alireza Mohammadi ◽  
Reza Saemi ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Urban Area ◽  
Incidence Rates ◽  
Low Risk ◽  
Health Concern ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Targeted Interventions ◽  
Risk Areas

Abstract Background:Cutaneous leishmaniasis (CL) is an important public health concern worldwide. Iran is among the most CL-affected countries, being listed as one of the first six endemic countries in the world. In order to develop targeted interventions, we performed a spatial-time visualization of CL cases in an urban area to identify high-risk and low-risk areas during 2016-2019.Methods:This cross-sectional study was conducted in the city of Mashhad. Patient data were gathered from Mashhad health centers. All cases (n=2425) were diagnosed in two stages; the initial diagnosis was based on clinical findings. Subsequently, clinical manifestation was confirmed by parasitological tests. The data were aggregated at the neighborhood and district levels and smoothed CL incidence rates per 100,000 individuals were calculated using the spatial empirical Bayesian approach. Furthermore, we used the Anselin Local Moran’s I statistic to identify clusters and outliers of CL distribution during 2016-2019 in Mashhad. Results:The overall incidence rates decreased from 34.6 per 100,000 in 2016 to 19.9 per 100,000 individuals in 2019. Both cluster analyses by crude incidence rate and smoothed incidence rate identified high-risk areas in southwestern Mashhad over the study period. Furthermore, the analyses revealed low-risk areas in northeastern Mashhad over the same 3-year period.Conclusions:The southwestern area of Mashhad had the highest CL incidence rates. This piece of information might be of value to design tailored interventions such as running effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generates new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in areas with higher associated risks.

scholarly journals Implications of Projected Hydroclimatic Change for Tularemia Outbreaks in High-Risk Areas across Sweden

2020 ◽  
Vol 17 (18) ◽  
pp. 6786
Author(s):  
Yan Ma ◽  
Guillaume Vigouroux ◽  
Zahra Kalantari ◽  
Romain Goldenberg ◽  
Georgia Destouni
Keyword(s):  
High Risk ◽  
Power Law ◽  
Radiative Forcing ◽  
Disease Outbreaks ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Projections ◽  
Site Specific ◽  
Risk Areas ◽  
Hydroclimatic Change

Hydroclimatic change may affect the range of some infectious diseases, including tularemia. Previous studies have investigated associations between tularemia incidence and climate variables, with some also establishing quantitative statistical disease models based on historical data, but studies considering future climate projections are scarce. This study has used and combined hydro-climatic projection outputs from multiple global climate models (GCMs) in phase six of the Coupled Model Intercomparison Project (CMIP6), and site-specific, parameterized statistical tularemia models, which all imply some type of power-law scaling with preceding-year tularemia cases, to assess possible future trends in disease outbreaks for six counties across Sweden, known to include tularemia high-risk areas. Three radiative forcing (emissions) scenarios are considered for climate change projection until year 2100, incuding low (2.6 Wm−2), medium (4.5 Wm−2), and high (8.5 Wm−2) forcing. The results show highly divergent changes in future disease outbreaks among Swedish counties, depending primarily on site-specific type of the best-fit disease power-law scaling characteristics of (mostly positive, in one case negative) sub- or super-linearity. Results also show that scenarios of steeper future climate warming do not necessarily lead to steeper increase of future disease outbreaks. Along a latitudinal gradient, the likely most realistic medium climate forcing scenario indicates future disease decreases (intermittent or overall) for the relatively southern Swedish counties Örebro and Gävleborg (Ockelbo), respectively, and disease increases of considerable or high degree for the intermediate (Dalarna, Gävleborg (Ljusdal)) and more northern (Jämtland, Norrbotten; along with the more southern Värmland exception) counties, respectively.

scholarly journals Dynamics of host populations affected by the emerging fungal pathogen Batrachochytrium salamandrivorans

2017 ◽  
Vol 4 (3) ◽  
pp. 160801 ◽  
Author(s):  
Benedikt R. Schmidt ◽  
Claudio Bozzuto ◽  
Stefan Lötters ◽  
Sebastian Steinfartz
Keyword(s):  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Host Population ◽  
Mitigation Measures ◽  
Disease Emergence ◽  
Batrachochytrium Salamandrivorans ◽  
In The Wild ◽  
Successful Control ◽  
Using Data ◽  
Local Extirpation

Emerging infectious diseases cause extirpation of wildlife populations. We use an epidemiological model to explore the effects of a recently emerged disease caused by the salamander-killing chytrid fungus Batrachochytrium salamandrivorans ( Bsal ) on host populations, and to evaluate which mitigation measures are most likely to succeed. As individuals do not recover from Bsal , we used a model with the states susceptible, latent and infectious, and parametrized the model using data on host and pathogen taken from the literature and expert opinion. The model suggested that disease outbreaks can occur at very low host densities (one female per hectare). This density is far lower than host densities in the wild. Therefore, all naturally occurring populations are at risk. Bsal can lead to the local extirpation of the host population within a few months. Disease outbreaks are likely to fade out quickly. A spatial variant of the model showed that the pathogen could potentially spread rapidly. As disease mitigation during outbreaks is unlikely to be successful, control efforts should focus on preventing disease emergence and transmission between populations. Thus, this emerging wildlife disease is best controlled through prevention rather than subsequent actions.

scholarly journals Indoor dust as a matrix for surveillance of COVID-19 outbreaks

2021 ◽  
Author(s):  
Nicole Renninger ◽  
Nick Nastasi ◽  
Ashleigh Bope ◽  
Samuel J. Cochran ◽  
Sarah R. Haines ◽  
...  
Keyword(s):  
High Risk ◽  
Geometric Mean ◽  
Disease Outbreaks ◽  
Viral Disease ◽  
Passive Sampler ◽  
Indoor Dust ◽  
Detection Methods ◽  
Environmental Surveillance ◽  
Floor Dust ◽  
High Risk Populations

AbstractOngoing disease surveillance is a critical tool to mitigate viral outbreaks, especially during a pandemic. Environmental monitoring has significant promise even following widespread vaccination among high-risk populations. The goal of this work is to demonstrate molecular SARS-CoV-2 monitoring in bulk floor dust and related samples as a proof-of-concept of a non-invasive environmental surveillance methodology for COVID-19 and potentially other viral diseases. Surface swab, passive sampler, and bulk floor dust samples were collected from rooms of individuals infected with COVID-19, and SARS-CoV-2 was measured with quantitative reverse transcription polymerase chain reaction (RT-qPCR) and two digital PCR (dPCR) methods. Bulk dust samples had geometric mean concentration of 159 copies/mg-dust and ranged from non-detects to 23,049 copies/mg-dust detected using ddPCR. An average of 88% of bulk dust samples were positive for the virus among detection methods compared to 55% of surface swabs and fewer on the passive sampler (19% carpet, 29% polystyrene). In bulk dust, SARS-CoV-2 was detected in 76%, 93%, and 97% of samples measured by qPCR, chip-based dPCR, and droplet dPCR respectively. Detectable viral RNA in the bulk vacuum bags did not measurably decay over 4 weeks, despite the application of a disinfectant before room cleaning. Future monitoring efforts should further evaluate RNA persistence and heterogeneity in dust. This study did not measure virus viability in dust or potential transmission associated with dust. Overall, this work demonstrates that bulk floor dust is a potentially useful matrix for long-term monitoring of viral disease outbreaks in high-risk populations and buildings.ImportanceEnvironmental surveillance to assess pathogen presence within a community is proving to be a critical tool to protect public health, and it is especially relevant during the ongoing COVID-19 pandemic. Importantly, environmental surveillance tools also allow for the detection of asymptomatic disease carriers and for routine monitoring of a large number of people as has been shown for SARS-CoV-2 wastewater monitoring. However, additional monitoring techniques are needed to screen for outbreaks in high-risk settings such as congregate care facilities. Here, we demonstrate that SARS-CoV-2 can be detected in bulk floor dust collected from rooms housing infected individuals. This analysis suggests that dust may be a useful and efficient matrix for routine surveillance of viral disease outbreaks.

scholarly journals Emerging viral infectious disease threat: Why Tanzania is not in a safe zone

2016 ◽  
Vol 18 (3) ◽  
Author(s):  
Chacha D. Mangu ◽  
Christina K. Manyama ◽  
Henry Msila ◽  
Lwitiho Sudi ◽  
Godlove Chaula ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Early Response ◽  
Emerging Diseases ◽  
Surveillance Systems ◽  
Viral Infectious Disease ◽  
The Government

Emerging diseases are global threat towards human existence. Every country is exposed to potentially emergence of infectious diseases. Several factor such as changes in ecology, climate and human demographics play different roles in a complex mechanism contributing to the occurrence of infectious diseases. Important aspects towards control in case of outbreaks are surveillance, preparedness and early response. Tanzania should therefore take opportunity of the calm situation currently present, to prepare. Except for HIV/AIDS, Tanzania has not experienced a major public health threat. However, the question is, is the country safe from emerging and re-emerging infectious diseases? In this article we try to explore the danger of emerging infectious disease (EID) epidemics in Tanzania and the risks attached if an outbreak is to occur. The aim is to formulate recommendations to the government, responsible authorities and general population of what can be done to improve the level of EID preparedness in the country. In conclusion, it is important to strengthen the capacity of community and healthcare staffs on how to respond to potential infectious disease outbreaks. Community-based surveillance systems should be incorporated into the national systems for early detection of public health events. It is also critical to enhance one health approach to increase cross-sectoral information sharing, surveillance and interventional strategies as regards to preparedness and response to disease outbreaks.

scholarly journals Relative Risk Estimation for Human Leptospirosis Disease in Malaysia Based on Existing Models and Discrete Space-Time Stochastic Sir Model

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Sufi Hafawati Ideris ◽  
Muhammad Rozi Malim ◽  
Norshahida Shaadan
Keyword(s):  
Relative Risk ◽  
High Risk ◽  
Risk Estimation ◽  
Information Criteria ◽  
Control Measures ◽  
Transmission Model ◽  
Surveillance Systems ◽  
Relative Risks ◽  
Risk Estimates ◽  
Risk Areas

The disease leptospirosis is known to be endemic in Malaysia, and it significantly impacts human wellbeing and the national economy. Current surveillance systems are based on morbidity and mortality leptospirosis national data from the Ministry of Health and remain inadequate due to the number of unreported and misdiagnosed cases. A robust surveillance system is needed to monitor temporal and spatial changes which yield improvements in terms of identifying high-risk areas and disease behaviour. The objective of this study is to identify high-risk areas by estimating relative risk using existing models which are the Standardized Morbidity Ratio (SMR), Poisson-gamma, log-normal, Besag, York and Mollié (BYM) and mixture models. An alternative model is also proposed which involves transmission systems and stochastic elements, namely the stochastic Susceptible-Infected-Removed (SIR) transmission model. This estimation of risk is expected to assist in the early detection of high-risk areas which can be applied as a strategy for preventive and control measures. The methodology in this paper applies relative risk estimates to determine the infection risk for all states in Malaysia based on monthly data from 2011 to 2018 using WinBUGS 1.4 software. The results of relative risks are discussed and presented in tables and graphs for each model to disclose high-risk areas across the country. Based on the risk estimates, different models used have different risk interpretations and drawbacks which make each model different in its use depending on the objectives of the study. As a result, the deviance information criteria (DIC) values obtained do not differ greatly from each expected risk which was estimated

scholarly journals Global Impact and Implications of Covid-19: Call for Multi-Dimensional Perspectives towards Sustainable Solutions

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Teddie O. Rahube
Keyword(s):  
Disease Surveillance ◽  
Specific Treatment ◽  
Disease Outbreaks ◽  
Developed Countries ◽  
Viral Disease ◽  
Health Impact ◽  
Late Response ◽  
Surveillance Systems ◽  
African Countries ◽  
Sustainable Solutions

The COVID-19 pandemic poses an enormous challenge, and it is evidently presenting itself as one of the greatest threats to humanity. The aim of this paper is to review the current state of the COVID-19 pandemic, the global health impact and implications of COVID-19 relative to other recent viral disease outbreaks and antimicrobial resistance (AMR), with the aim to propose the implementation of sustainable solutions. The magnitude of COVID-19 deaths is incomparable to other coronaviruses (CoVs) disease outbreaks experienced in recent history. The high number of deaths observed in developed countries compared to developing countries may have been triggered by the late response/preparedness to the pandemic rather than by the socio-economic statuses. CoVs will remain a serious health threat to humanity due to absence of vaccines and anti-viral treatments. The absence of specific treatment regimens also lead to heavy reliance on chemical disinfectants and could significantly contribute to the rise in AMR, further raising some important questions surrounding hygiene, microbes, ecosystem health and human diseases. The CEASE approach, comprising of five key elements; Communication, Education, Advocacy, Socialization, and Experimentation is proposed for implementation at a global level. CEASE approach is critical especially for African countries and can be used to further explore opportunities that can lead to improvements in sanitation, access to clean water, health care, education and infectious disease surveillance systems.

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

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.

scholarly journals Using network theory to identify the causes of disease outbreaks of unknown origin

2013 ◽  
Vol 10 (81) ◽  
pp. 20120904 ◽  
Author(s):  
Tiffany L. Bogich ◽  
Sebastian Funk ◽  
Trent R. Malcolm ◽  
Nok Chhun ◽  
Jonathan H. Epstein ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Nipah Virus ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Case Fatality ◽  
Unknown Origin ◽  
Surveillance Systems ◽  
Predictive Values ◽  
Rapid Responses ◽  
Causes Of Disease

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic.

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