Risk management, signal processing and econometrics: A new tool for forecasting the risk of disease outbreaks

As businesses attempt to reopen to varying degrees amid the current coronavirus disease (COVID-19) pandemic, industrial hygiene (IH) and occupational and environmental health and safety (OEHS) professionals have been challenged with assessing and managing the risks of COVID-19 in the workplace. In general, the available IH/OEHS tools were designed to control hazards originating in the workplace; however, attempts to tailor them specifically to the control of infectious disease outbreaks have been limited. This analysis evaluated the IH decision-making framework (Anticipate, Recognize, Evaluate, Control, and Confirm (“ARECC”)) as it relates to biological hazards, in general, and to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), specifically. Available IH/OEHS risk assessment and risk management tools (e.g. control banding and the hierarchy of controls) are important components of the ARECC framework. These conceptual models, however, were primarily developed for controlling chemical hazards and must be adapted to the unique characteristics of highly infectious and virulent pathogens, such as SARS-CoV-2. This assessment provides an overview of the key considerations for developing occupational infection control plans, selecting the best available controls, and applying other emerging tools (e.g. quantitative microbial risk assessment), with the ultimate goal of facilitating risk management decisions during the current global pandemic.

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Managing the endogenous risk of disease outbreaks with non-constant background risk

Journal of Economic Dynamics and Control ◽

10.1016/j.jedc.2014.09.014 ◽

2015 ◽

Vol 51 ◽

pp. 166-179 ◽

Cited By ~ 8

Author(s):

Kevin Berry ◽

David Finnoff ◽

Richard D. Horan ◽

Jason F. Shogren

Keyword(s):

Disease Outbreaks ◽

Background Risk ◽

Endogenous Risk ◽

Risk Of Disease

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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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Appraisal of Chicken Production with Associated Biosecurity Practices in Commercial Poultry Farms Located in Jos, Nigeria

Scientifica ◽

10.1155/2016/1914692 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

C. V. Maduka ◽

I. O. Igbokwe ◽

N. N. Atsanda

Keyword(s):

Production System ◽

Disease Outbreaks ◽

Flock Size ◽

System A ◽

Poultry Farms ◽

Commercial Farms ◽

Weak Points ◽

Commercial Poultry ◽

Risk Of Disease ◽

Biosecurity Practices

A questionnaire-based study of chicken production system with on-farm biosecurity practices was carried out in commercial poultry farms located in Jos, Nigeria. Commercial and semicommercial farms had 75.3% and 24.5% of 95,393 birds on 80 farms, respectively. Farms using deep litter and battery cage systems were 69 (86.3%) and 10 (12.5%), respectively. In our biosecurity scoring system, a correct practice of each indicator of an event scored 1.00 and biosecurity score (BS) of each farm was the average of the scores of biosecurity indicators for the farm, giving BS of zero and 1.00 as absence of biosecurity and optimal biosecurity, respectively. Semicommercial farms had higher BS than commercial farms. The flock size did not significantly (p>0.05) affect the mean BS. Disease outbreaks correlated (r=-0.97) with BS, showing a tendency of reduction of disease outbreaks with increasing BS. Outbreaks were significantly (p<0.05) associated with deep litter system. In conclusion, the chicken production system requires increased drive for excellent biosecurity practices and weak points in the biosecurity could be ameliorated by extension of information to farmers in order to support expansion of chicken production with robust biosecurity measures that drastically reduce risk of disease outbreak.

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Strengths and limitations of molecular subtyping in a community outbreak of Legionnaires' disease

Epidemiology and Infection ◽

10.1017/s095026880000474x ◽

2000 ◽

Vol 125 (3) ◽

pp. 599-608 ◽

Cited By ~ 18

Author(s):

J. L. KOOL ◽

U. BUCHHOLZ ◽

C. PETERSON ◽

E. W. BROWN ◽

R. F. BENSON ◽

...

Keyword(s):

Los Angeles ◽

Case Control Study ◽

Disease Outbreaks ◽

Cooling Towers ◽

Outbreak Strain ◽

Source Of Infection ◽

Outbreak Area ◽

Risk Of Disease ◽

Legionnaires Disease ◽

Control Study

An epidemiological and microbiological investigation of a cluster of eight cases of Legionnaires' disease in Los Angeles County in November 1997 yielded conflicting results. The epidemiological part of the investigation implicated one of several mobile cooling towers used by a film studio in the centre of the outbreak area. However, water sampled from these cooling towers contained L. pneumophila serogroup 1 of another subtype than the strain that was recovered from case-patients in the outbreak. Samples from two cooling towers located downwind from all of the case-patients contained a Legionella strain that was indistinguishable from the outbreak strain by four subtyping techniques (AP-PCR, PFGE, MAb, and MLEE). It is unlikely that these cooling towers were the source of infection for all the case-patients, and they were not associated with risk of disease in the case-control study. The outbreak strain also was not distinguishable, by three subtyping techniques (AP-PCR, PFGE, and MAb), from a L. pneumophila strain that had caused an outbreak in Providence, RI, in 1993. Laboratory cross-contamination was unlikely because the initial subtyping was done in different laboratories.In this investigation, microbiology was helpful for distinguishing the outbreak cluster from unrelated cases of Legionnaires' disease occurring elsewhere. However, multiple subtyping techniques failed to distinguish environmental sources that were probably not associated with the outbreak. Persons investigating Legionnaires' disease outbreaks should be aware that microbiological subtyping does not always identify a source with absolute certainty.

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Modelling forest degradation and risk of disease outbreaks in mainland Equatorial Guinea

Journal of Public Health and Emergency ◽

10.21037/jphe-20-97 ◽

2021 ◽

Vol 5 ◽

pp. 15-15

Author(s):

Ophélie Poirier ◽

Rafael Ruiz de Castañeda ◽

Isabelle Bolon ◽

Nicolas Ray

Keyword(s):

Disease Outbreaks ◽

Forest Degradation ◽

Equatorial Guinea ◽

Risk Of Disease

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Modeling Spatial Frailties in Survival Analysis of Cucurbit Downy Mildew Epidemics

Phytopathology ◽

10.1094/phyto-07-12-0152-r ◽

2013 ◽

Vol 103 (3) ◽

pp. 216-227 ◽

Cited By ~ 13

Author(s):

P. S. Ojiambo ◽

E. L. Kang

Keyword(s):

United States ◽

Phase I ◽

Downy Mildew ◽

Spatial Dependence ◽

Disease Outbreaks ◽

Frailty Model ◽

Disease Outbreak ◽

Cucurbit Downy Mildew ◽

Spatially Correlated ◽

Risk Of Disease

Cucurbit downy mildew caused by Pseudoperonospora cubensis is economically the most important disease of cucurbits globally, and the pathogen is disseminated aerially over a large spatial scale. Spatio-temporal spread of the disease was characterized during phase I (low and sporadic disease outbreaks) and II (rapid increase in disease outbreaks) of the epidemic using records collected from sentinel plots from 2008 to 2009 in 23 states in the eastern United States as part of the United States Department of Agriculture Cucurbit Downy Mildew ipmPIPE network. A substantive goal of this study was to explain the pattern of time to disease outbreak using important covariates while accounting for spatially correlated differences in risk of disease outbreak among the states. Survival analyses that accounts for spatial dependence were performed on time to disease outbreak, and posterior median frailties (or random effects) were mapped to identify states with high or low risk for disease outbreak. From February to October, disease occurred in 195 and 172 out of 413 and 556 cases monitored in 2008 and 2009, respectively. Disease outbreaks were spatially aggregated, with a spatial dependence of up to ≈1,025 km where clustering of outbreaks in phase I and II of the epidemic were similar. However, unlike in phase I of the epidemic, space–time point pattern analysis was significant (P < 0.0001) for outbreaks in phase II, during which the highest risk window as estimated by the space–time function was within 1.5 months and 500 km of the initial outbreak. The risk of disease outbreak peaked around July and decreased thereafter until the end of the study period. Spatially correlated analysis of time to disease outbreak indicated the need to incorporate spatial frailties in standard survival analysis models. Evaluation of alternative formulations of the spatial models demonstrated that a Bayesian hierarchical spatially structured frailty model best described time to disease outbreak. This frailty model showed clustering of outbreaks at the state level and indicated that states in the mid-Atlantic region have high spatial frailties and a high risk of downy mildew outbreak.

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Forests can help diminish the number of dengue cases in Costa Rica

10.21203/rs.3.rs-141798/v1 ◽

2021 ◽

Author(s):

Matias Piaggio ◽

Eduardo Pacay ◽

Juan Robalino ◽

Taylor Ricketts

Keyword(s):

Costa Rica ◽

Forest Conservation ◽

Forest Cover ◽

Hospital Admissions ◽

Disease Outbreaks ◽

Health Care Expenditures ◽

Anthropogenic Habitats ◽

Global Pandemic ◽

Risk Of Disease ◽

Dengue Epidemic

Abstract Approximately 3.9 billion people are at risk of infection with dengue fever, a group of viruses transmitted by mosquitos.1,2 In 2019 Central America has suffered a strong dengue epidemic.3 Costa Rica has almost doubled the number of dengue cases between in the first 24 epidemilogical weeks of 2019 regarding the same period in the previous year.4 In the Americas, forest cover is thought to diminish anthropogenic habitats for mosquito larvae, as well as increase the presence of their predators.5,6 Here we estimate the marginal effects of increasing forest cover on dengue prevalence, using econometric models to relate hospital admission records and forest cover maps from 2001 and 2011. We find that increasing the percentage of forest cover significantly decreased both the number of hospital admissions for dengue and the probability of an outbreak. Using the same models, we predict that increasing forest cover by one percentage point would have avoided between 85 to 103 dengue hospital admissions per year. This represents savings between USD 21,500 to 295,000 per year, depending on the severity of dengue cases. Our study shows that forest conservation can be a public health investment that increases welfare both by avoiding sickness and by reducing associated health care expenditures. Understanding the contribution of nature to diminish the risk of disease outbreaks turn even more urgent and important under the COVID-19 global pandemic the world has faced in 2020.7,8

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Heterogeneity in social and epidemiological factors determines the risk of measles outbreaks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1920986117 ◽

2020 ◽

Vol 117 (48) ◽

pp. 30118-30125

Author(s):

Paolo Bosetti ◽

Piero Poletti ◽

Massimo Stella ◽

Bruno Lepri ◽

Stefano Merler ◽

...

Keyword(s):

Large Scale ◽

Disease Outbreaks ◽

Mobile Phone Data ◽

Epidemic Dynamics ◽

Effective Strategies ◽

Regional Conflicts ◽

Societal Challenges ◽

Risk Of Disease ◽

Measles Outbreaks ◽

Vaccination Campaigns

Political and environmental factors—e.g., regional conflicts and global warming—increase large-scale migrations, posing extraordinary societal challenges to policymakers of destination countries. A common concern is that such a massive arrival of people—often from a country with a disrupted healthcare system—can increase the risk of vaccine-preventable disease outbreaks like measles. We analyze human flows of 3.5 million (M) Syrian refugees in Turkey inferred from massive mobile-phone data to verify this concern. We use multilayer modeling of interdependent social and epidemic dynamics to demonstrate that the risk of disease reemergence in Turkey, the main host country, can be dramatically reduced by 75 to 90% when the mixing of Turkish and Syrian populations is high. Our results suggest that maximizing the dispersal of refugees in the recipient population contributes to impede the spread of sustained measles epidemics, rather than favoring it. Targeted vaccination campaigns and policies enhancing social integration of refugees are the most effective strategies to reduce epidemic risks for all citizens.

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The Relationship between Environmental Characteristics and Risk Management Practices on Produce Farms: A Systematic Literature Review

Agriculture ◽

10.3390/agriculture10120577 ◽

2020 ◽

Vol 10 (12) ◽

pp. 577

Author(s):

Dilhani Nisansala Jayawardhana ◽

Loan Thi Thanh Cao ◽

Thomas A. Yeargin ◽

Kristen E. Gibson ◽

Angela M. Fraser

Keyword(s):

Risk Management ◽

Management Practices ◽

Disease Outbreaks ◽

Developed Countries ◽

Study Data ◽

Training Data ◽

Environmental Characteristic ◽

Environmental Characteristics ◽

Physical Resources ◽

The Relationship

Produce-associated foodborne disease outbreaks have increased worldwide highlighting the importance of proper implementation of risk management practices (RMP). We determined the relationship between environmental characteristics (i.e., physical resources) of produce farms and implementation of RMP. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses principles, we identified 36 studies to include in our analysis. Most study data were collected through surveys administered to growers in developed countries. Quality assessment results showed that studies on this topic should be more rigorously conducted (e.g., powering sample sizes and training data collectors) to yield better quality evidence. Agricultural waters were the most common environmental characteristic assessed, with many farms using unsafe water sources. Hygiene aids (e.g., accessible handwashing facilities), were lacking across many farms. Animal intrusion RMP were the least commonly assessed environmental characteristic. Only one study tested the relationship between on-farm environmental characteristics and RMP implementation reporting a positive relationship between accessible handwashing and worker hygiene practices. Grower knowledge and perception of RMP combined with cost and ease in carrying out RMP might influence the availability of physical resources for proper RMP implementation. These results can inform practical interventions aimed to increase adoption of RMP on produce farms.

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