Xenotropic Murine Leukemia Virus-Related Virus as a Case Study: Using a Precautionary Risk Management Approach for Emerging Blood-Borne Pathogens in Canada

In October 2009 it was reported that 68 of 101 patients with chronic fatigue syndrome (CFS) in the United States, when tested, were infected with a novel gamma retrovirus, xenotropic murine leukemia virus-related virus (XMRV) (Lombardi et al., 2009). XMRV is a recently discovered human gammaretrovirus first described in prostate cancers that shares significant homology with murine leukemia virus (MLV) (Ursiman et al., 2006). It is known that XMRV can cause leukemias and sarcomas in several rodent, feline, and primate species but has not been shown to cause disease in humans. XMRV was detectable in the peripheral blood mononuclear cells (PBMCs) and plasma of individuals diagnosed with CFS (Lombardi et al., 2009). After this report was published there was a great deal of uncertainty surrounding this emergent virus and its involvement in the etiology of CFS. The uncertainty was, in part, due to CFS being a complex, poorly understood multi-system disorder with different disease criteria used for its diagnosis. CFS, also known as Myalgic Encephalomyelitis (ME), is a debilitating disease of unknown origin that is estimated to affect 17 million people worldwide. The initial report connecting XMRV to prostate cancers and CFS garnered significant media and scientific interest since it provided a potential Susie ElSaadany2**, Tamer Oraby1 * Daniel Krewski1, 4 and Peter R. Ganz5 1McLaughlin Centre for Population Health Risk Assessment, Institute of Population Health, University of Ottawa, Ontario, Canada 2Blood Safety Surveillance and Health Care Acquired Infections Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Ottawa, Ontario, Canada 3Aspinall and Associates, Cleveland House, High Street, and Earth Sciences, Bristol University, Bristol, United Kingdom 4Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada 5Health Canada, Director’s Office, Ottawa, Ontario, Canada ** Corresponding Author , Marian Laderoute2 , Jun Wu2 , Willy Aspinall3 , www.intechopen.com 32 The Continuum of Health Risk Assessments explanation for the disease but also an avenue for possible therapeutic treatments since XMRV is known to be susceptible to some anti-retroviral drugs (Cohen, 2011)

Modeling the effect of lockdown timing as a COVID‑19 control measure in countries with differing social contacts

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the R 0 formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible R 0 for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a “tunneling” effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well

Using a stochastic continuous-time Markov chain model to examine alternative timing and duration of the COVID-19 lockdown in Kuwait: what can be done now?

Background Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. Methods The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. Results The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. Conclusions In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations

Expert elicitation on the uncertainties associated with chronic wasting disease

A high degree of uncertainty exists for chronic wasting disease (CWD) transmission factors in farmed and wild cervids. Evaluating the factors is important as it helps to inform future risk management strategies. Expert opinion is often used to assist decision making in a number of health, science, and technology domains where data may be sparse or missing. Using the Classical Model of elicitation, a group of experts was asked to estimate the most likely values for several risk factors affecting CWD transmission. The formalized expert elicitation helped structure the issues and hence provide a rational basis for estimating some transmission risk factors for which evidence is lacking. Considered judgments regarding environmental transmission, latency of CWD transmission, management, and species barrier were provided by the experts. Uncertainties for many items were determined to be large, highlighting areas requiring more research. The elicited values may be used as surrogate values until research evidence becomes available

Analysis of the Healthcare MERS-CoV Outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June–August 2015 Using a SEIR Ward Transmission Model

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus that has a tendency to cause significant healthcare outbreaks among patients with serious comorbidities. We analyzed hospital data from the MERS-CoV outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June–August 2015 using the susceptible-exposed-infectious-recovered (SEIR) ward transmission model. The SEIR compartmental model considers several areas within the hospital where transmission occurred. We use a system of ordinary differential equations that incorporates the following units: emergency department (ED), out-patient clinic, intensive care unit, and hospital wards, where each area has its own carrying capacity and distinguishes the transmission by three individuals in the hospital: patients, health care workers (HCW), or mobile health care workers. The emergency department, as parameterized has a large influence over the epidemic size for both patients and health care workers. Trend of the basic reproduction number (R0), which reached a maximum of 1.39 at the peak of the epidemic and declined to 0.92 towards the end, shows that until added hospital controls are introduced, the outbreak would continue with sustained transmission between wards. Transmission rates where highest in the ED, and mobile HCWs were responsible for large part of the outbreak

On the performance of social network and likelihood-based expert weighting schemes

Using expert judgment data from the TU Delft's expert judgment database, we compare the performance of different weighting schemes, namely equal weighting, performance-based weighting from the classical model [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991.], social network (SN) weighting and likelihood weighting. The picture that emerges with regard to SN weights is rather mixed. SN theory does not provide an alternative to performance-based combination of expert judgments, since the statistical accuracy of the SN decision maker is sometimes unacceptably low. On the other hand, it does outperform equal weighting in the majority of cases. The results here, though not overwhelmingly positive, do nonetheless motivate further research into social interaction methods for nominating and weighting experts. Indeed, a full expert judgment study with performance measurement requires an investment in time and effort, with a view to securing external validation. If high confidence in a comparable level of validation can be obtained by less intensive methods, this would be very welcome, and would facilitate the application of structured expert judgment in situations where the resources for a full study are not available. Likelihood weights are just as resource intensive as performance-based weights, and the evidence presented here suggests that they are inferior to performance-based weights with regard to those scoring variables which are optimized in performance weights (calibration and information). Perhaps surprisingly, they are also inferior with regard to likelihood. Their use is further discouraged by the fact that they constitute a strongly improper scoring rule

A stochastic model of the bovine spongiform encephalopathy epidemic in Canada

<p>Bovine spongiform encephalopathy (BSE) appeared in the United Kingdom in the mid 1980s, and has been attributed to the use of meat and bone meal (MBM) in cattle feed contaminated with a scrapie-like agent. Import of infectious materials from a country where BSE has occurred is believed to be the major factor underlying the spread of the BSE epidemic to other countries. This study presents a new stochastic model developed to estimate risk of BSE from importation of cattle infected with the BSE agent. The model describes the propagation of the BSE agent through the Canadian cattle herd through rendering and feeding processes, following importation of cattle with infectious prions. This model was used estimate the annual number of newly infected animals each year over the period 1980–2019. Model predictions suggested that the number of BSE infections in Canada might have been approximately 40-fold greater than the actual number of clinically diagnosed cases. Under complete compliance with the 2007 ban on feeding MBM, this model further predicts that BSE is disappearing from the Canadian cattle system. A series of sensitivity analyses was also conducted to test the robustness of model predictions to alternative assumptions about factors affecting the evolution of the Canadian BSE epidemic.</p