scholarly journals Climate and the Global Spread and Impact of Bananas’ Black Leaf Sigatoka Disease

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 947
Author(s):  
Eric Strobl ◽  
Preeya Mohan
Keyword(s):  
Climatic Factors ◽  
Disease Incidence ◽  
Disease Spread ◽  
Climate Data ◽  
Black Sigatoka ◽  
Long Distance ◽  
Leaf Disease ◽  
Global Spread ◽  
The Impact ◽  
First Time

While Black Sigatoka Leaf Disease (Mycosphaerella fijiensis) has arguably been the most important pathogen affecting the banana industry over the past 50 years, there are no quantitative estimates of what risk factors determine its spread across the globe, nor how its spread has affected banana producing countries. This study empirically models the disease spread across and its impact within countries using historical spread timelines, biophysical models, local climate data, and country level agricultural data. To model the global spread a empirical hazard model is employed. The results show that the most important factor affecting first time infection of a country is the extent of their agricultural imports, having increased first time disease incidence by 69% points. In contrast, long distance dispersal due to climatic factors only raised this probability by 0.8% points. The impact of disease diffusion within countries once they are infected is modelled using a panel regression estimator. Findings indicate that under the right climate conditions the impact of Black Sigatoka Leaf Disease can be substantial, currently resulting in an average 3% reduction in global annual production, i.e., a loss of yearly revenue of about USD 1.6 billion.

scholarly journals Sheep scab spatial distribution: the roles of transmission pathways

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emily Joanne Nixon ◽  
Ellen Brooks-Pollock ◽  
Richard Wall
Keyword(s):  
Disease Transmission ◽  
Movement Control ◽  
Cost Effective ◽  
Infected Sheep ◽  
Disease Spread ◽  
Psoroptes Ovis ◽  
Long Distance ◽  
Sheep Scab ◽  
The Uk ◽  
The Impact

Abstract Background Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep–environment contact and through long-distance movements of infected sheep, such as through markets. Methods A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. Results Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. Conclusions Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management. Graphical Abstract

scholarly journals Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone

2020 ◽  
Vol 117 (9) ◽  
pp. 5067-5073 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
Using Data ◽  
The Impact

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals Incubation periods impact the spatial predictability of outbreaks: analysis of cholera and Ebola outbreaks in Sierra Leone

2019 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
The Impact

AbstractForecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.Significance statementUnderstanding how infectious diseases spread is critical for preventing and containing outbreaks. While advances have been made in forecasting epidemics, much is still unknown. Here we show that the incubation period – the time between exposure to a pathogen and onset of symptoms – is an important factor in predicting spatiotemporal spread of disease and provides one explanation for the different trajectories of the recent Ebola and cholera outbreaks in Sierra Leone. We find that outbreaks of pathogens with longer incubation periods, such as Ebola, tend to have less predictable spread, whereas pathogens with shorter incubation periods, such as cholera, spread in a more predictable, wavelike pattern. These findings have implications for the scale and timing of reactive interventions, such as vaccination campaigns.

scholarly journals Impact of non-pharmaceutical interventions against COVID-19 in Europe in 2020: a quasi-experimental non-equivalent group and time series design study

2021 ◽  
Vol 26 (28) ◽  
Author(s):  
Paul R Hunter ◽  
Felipe J Colón-González ◽  
Julii Brainard ◽  
Steven Rushton
Keyword(s):  
Social Life ◽  
Control Strategies ◽  
Disease Incidence ◽  
Disease Spread ◽  
Social Distancing ◽  
Mass Gatherings ◽  
Quasi Experimental ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
At Home

Introduction The current pandemic of coronavirus disease (COVID-19) is unparalleled in recent history as are the social distancing interventions that have led to a considerable halt on the economic and social life of so many countries. Aim We aimed to generate empirical evidence about which social distancing measures had the most impact in reducing case counts and mortality. Methods We report a quasi-experimental (observational) study of the impact of various interventions for control of the outbreak through 24 April 2020. Chronological data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Prevention and Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our complementary analyses were modelled in R using Bayesian generalised additive mixed models and in STATA using multilevel mixed-effects regression models. Results From both sets of modelling, we found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay-at-home orders and closure of additional non-essential businesses was not associated with any independent additional impact. Conclusions Our findings are that schools and some non-essential businesses operating ‘as normal’ as well as allowing mass gatherings were incompatible with suppressing disease spread. Closure of all businesses and stay at home orders are less likely to be required to keep disease incidence low. Our results help identify what were the most effective non-pharmaceutical interventions in this period.

scholarly journals Perspectives on Applying Artificial Intelligence to Model Outbreak Risk: Contextualizing the Impact of Climate Change on Vector-borne Disease

2020 ◽  
Author(s):  
Alberto Alexander Gayle
Keyword(s):  
Public Health ◽  
Climate Change ◽  
West Nile Virus ◽  
Large Scale ◽  
Climatic Factors ◽  
Disease Spread ◽  
Behavioral Variability ◽  
Climatic Effects ◽  
West Nile ◽  
The Impact

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

Floods Increase the Risks of Hand-Foot-Mouth Disease in Qingdao, China, 2009-2013: A Quantitative Analysis

2018 ◽  
Vol 12 (6) ◽  
pp. 723-729 ◽  
Author(s):  
Xiaowen Hu ◽  
Fachun Jiang ◽  
Wei Ni
Keyword(s):  
Climatic Factors ◽  
Mouth Disease ◽  
Study Region ◽  
Relative Risks ◽  
Hand Foot Mouth Disease ◽  
Spearman Correlation Test ◽  
Lagged Effects ◽  
Different Populations ◽  
The Impact ◽  
First Time

AbstractBackgroundWe aimed to quantify the impact of few times floods on hand-foot-mouth disease (HFMD) in Qingdao during 2009-2013.MethodsThe Spearman correlation test was applied to examine the lagged effects of floods on monthly morbidity of HFMD during study period in Qingdao. We further quantified the effects of 5 flood events on the morbidity of HFMD using the time-series Poisson regression controlling for climatic factors, seasonality, and lagged effects among different populations.ResultsA total of 55,920 cases of HFMD were reported in the study region over the study period. The relative risks of floods on the morbidity of HFMD among the total population, males, females, under 1-2 years old, and 3-5 years old were 1.178, 1.165, 1.198, 1.338, and 1.245, respectively.ConclusionsThis study has, for the first time, provided the positive evidence of the impact of floods on HFMD. It demonstrates that floods can significantly increase the risk of HFMD during study period. Additionally, among the different populations, the risks were higher among children under 1-5 years old. (Disaster Med Public Health Preparedness. 2018;12:723-729)

scholarly journals Determination of the dependence between climatic factors and Covid-19 disease incidence

2021 ◽  
Vol 38 (2) ◽  
pp. 94-103
Author(s):  
Olena P. Mitryasova ◽  
Anna S. Pryhodko
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Climatic Factors ◽  
Disease Incidence ◽  
Inverse Correlation ◽  
Disease Spread ◽  
Scientific Methods

The purpose of research consists in definition and an estimation of climatic factors influence on disease incidence of Covid-19 on an example of Mykolaiv city. In research we used such scientific methods: theoretical methods: analysis, synthesis, monitoring, systematization, generalization. For research facility, were held by calculations based on software Microsoft Excel. The calculations were performed using the formula correlation. Results. The study examines the influence of climatic factors such as air temperature, humidity, solar radiation activity, wind speed, rainfall, and length of daylight. For the pair «Disease incidence – Temperature» the correlation coefficient is −0.74. For the pair «Disease incidence − Solar Radiation» correlation coefficient is −0.71. For the pair «Disease incidence – Daylight hours» correlation coefficient is −0.70. Humidity, as a derivative of air temperature, is evidenced by a comparison of decline periods and growth of these values. In the spring, along with the increase in temperature, the humidity dropped, and in the fall, when the air temperature dropped, the humidity increased. This factor also affected the spread of the virus in the second half of the year, when the humidity increased the virus began to spread faster. For the pair «Disease incidence – Humidity» correlation coefficient is +0.73 (average direct correlation). Other climatic factors, such as wind speed and rainfall, have not been shown to have a significant effect on the rate of disease spread. For the pair «Disease incidence − Wind speed» correlation coefficient is +0.32, which corresponds to a weak direct correlation. For the pair «Disease incidence − Rainfall» correlation coefficient is −0.30, which indicates a weak inverse correlation. Conclusion. The results of the study show that the reduction of disease incidence is observed at high temperatures, high activity of solar radiation, and prolonged daylight, which determines the conditions for the prevention of such diseases and will improve the quality of life to achieve sustainable development.

scholarly journals A dynamic, climate-driven model of Rift Valley fever

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Anne E. Jones ◽  
Cyril Caminade ◽  
Andrew P. Morse
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Climatic Factors ◽  
Model Development ◽  
Eastern Africa ◽  
Climate Data ◽  
Valley Fever ◽  
Model Driven ◽  
Spatio Temporal ◽  
The Impact

Outbreaks of Rift Valley fever (RVF) in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF) model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

scholarly journals Impact of the Timing of Spay-Neuter Related to Transport on Disease Rates in Relocated Dogs

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 630
Author(s):  
Erin Doyle ◽  
Maya Gupta ◽  
Miranda Spindel ◽  
Emily D. Dolan ◽  
Margaret R. Slater ◽  
...  
Keyword(s):  
Disease Risk ◽  
Surgical Stress ◽  
Surgical Complication ◽  
Disease Incidence ◽  
Companion Animals ◽  
Disease Spread ◽  
Disease Rates ◽  
Surgical Sterilization ◽  
Disease Exposure ◽  
The Impact

Companion animal relocation programs are an important method to address geographic and resource disparities in pet overpopulation through transport from areas with high homeless pet populations to areas with high adopter demand. Despite mitigation by following best practices, a potential risk of animal relocation is increased disease incidence related to infectious disease spread and the effects of stress during transport. Surgical sterilization may compound disease risk due to the impact of surgical stress on disease susceptibility and the potential for disease exposure from other patients. Our study aimed to provide information about disease and surgical complication incidence as relates to the timing of surgical sterilization in relocated dogs. A population of 431 dogs relocated to a shelter in Washington State was monitored for disease while at the destination shelter and immediately post-adoption. No increased disease incidence was identified for dogs altered within two weeks of transport at the destination shelter compared with those altered within two weeks prior to transport at the source shelter. Because of disparities addressed by relocation programs, surgical sterilization of relocated companion animals is typically best performed at the destination shelter. Our study indicates that disease incidence is not increased by spay-neuter at the destination shelter.

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