scholarly journals A simple model for how the risk of pandemics from different virus families depends on viral and human traits

2021 ◽  
Author(s):  
Julia Doelger ◽  
Arup K. Chakraborty ◽  
Mehran Kardar
Keyword(s):  
Wide Distribution ◽  
Health Interventions ◽  
Model Parameters ◽  
Animal Virus ◽  
Modes Of Transmission ◽  
Global Pandemic ◽  
Inflection Points ◽  
Threshold Time ◽  
Corona Viruses ◽  
Animal Reservoirs

AbstractDifferent virus families, like influenza or corona viruses, exhibit characteristic traits such as typical modes of transmission and replication as well as specific animal reservoirs in which each family of viruses circulate. These traits of genetically related groups of viruses influence how easily an animal virus can adapt to infect humans, how well novel human variants can spread in the population, and the risk of causing a global pandemic. Relating the traits of virus families to their risk of causing future pandemics, and identification of the key time scales within which public health interventions can control the spread of a new virus that could cause a pandemic, are obviously significant. We address these issues using a minimal model whose parameters are related to characteristic traits of different virus families. A key trait of viruses that “spillover” from animal reservoirs to infect humans is their ability to propagate infection through the human population (fitness). We find that the risk of pandemics emerging from virus families characterized by a wide distribution of the fitness of spillover strains is much higher than if such strains were characterized by narrow fitness distributions around the same mean. The dependences of the risk of a pandemic on various model parameters exhibit inflection points. We find that these inflection points define informative thresholds. For example, the inflection point in variation of pandemic risk with time after the spillover represents a threshold time beyond which global interventions would likely be too late to prevent a pandemic.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia

2021 ◽  
Author(s):  
Wei Luo ◽  
Zhaoyin Liu ◽  
Yuxuan Zhou ◽  
Yumin Zhao ◽  
Yunyue Elita Li ◽  
...  
Keyword(s):  
Public Health ◽  
Southeast Asia ◽  
Spatiotemporal Analysis ◽  
Health Interventions ◽  
Disease Spread ◽  
Epidemic Dynamics ◽  
Health Measures ◽  
Global Pandemic ◽  
Public Health Strategies ◽  
Time Scan

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.

Modelling the individual and collective dynamics of the propensity to offend

2010 ◽  
Vol 21 (4-5) ◽  
pp. 421-440 ◽  
Author(s):  
J.-P. NADAL ◽  
M. B. GORDON ◽  
J. R. IGLESIAS ◽  
V. SEMESHENKO
Keyword(s):  
Crime Rate ◽  
Developed Countries ◽  
Wide Distribution ◽  
Deterrent Effect ◽  
Model Parameters ◽  
Self Organized ◽  
Interacting Agents ◽  
Composite Parameter ◽  
Two Phases ◽  
The Individual

We introduce a general framework for modelling the dynamics of the propensity to offend in a population of (possibly interacting) agents. We consider that each agent has an ‘honesty index’ which parameterizes his probability of abiding by the law. This probability also depends on a composite parameter associated to the attractiveness of the crime outcome and of the crime setting (the context which makes a crime more or less likely to occur, such as the presence or not of a guardian). Within this framework we explore some consequences of the working hypothesis that punishment has a deterrent effect, assuming that, after a criminal act, an agent's honesty index may increase if he is caught and decrease otherwise. We provide both analytical and numerical results. We show that in the space of parameters characterizing the probability of punishment, there are two ‘phases’: one corresponding to a population with a low crime rate and the other to a population with a large crime rate. We speculate on the possible existence of a self-organized state in which, due to the society reaction against crime activities, the population dynamics would be stabilized on the critical line, leading to a wide distribution of propensities to offend in the population. In view of empirical works on the causes of the recent evolution of crime rates in developed countries, we discuss how changes of socio-economic conditions may affect the model parameters, and hence the crime rate in the population. We suggest possible extensions of the model that will allow us to take into account more realistic features.

scholarly journals Susceptibility of ferrets, cats, dogs, and different domestic animals to SARS-coronavirus-2

2020 ◽  
Author(s):  
Jianzhong Shi ◽  
Zhiyuan Wen ◽  
Gongxun Zhong ◽  
Huanliang Yang ◽  
Chong Wang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Severe Acute Respiratory Syndrome ◽  
Close Contact ◽  
Domestic Animals ◽  
Sars Coronavirus ◽  
Animal Management ◽  
Global Pandemic ◽  
Respiratory Droplets ◽  
Animal Reservoirs

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the infectious disease COVID-19, which was first reported in Wuhan, China in December, 2019. Despite the tremendous efforts to control the disease, COVID-19 has now spread to over 100 countries and caused a global pandemic. SARS-CoV-2 is thought to have originated in bats; however, the intermediate animal sources of the virus are completely unknown. Here, we investigated the susceptibility of ferrets and animals in close contact with humans to SARS-CoV-2. We found that SARS-CoV-2 replicates poorly in dogs, pigs, chickens, and ducks, but efficiently in ferrets and cats. We found that the virus transmits in cats via respiratory droplets. Our study provides important insights into the animal reservoirs of SARS-CoV-2 and animal management for COVID-19 control.

scholarly journals Transmission dynamics of SARS-COV-2 in China: impact of public health interventions

2020 ◽  
Author(s):  
Wenbao Wang ◽  
Yiqin Chen ◽  
Qi Wang ◽  
Ping Cai ◽  
Ye He ◽  
...  
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Public Health Interventions ◽  
The Public ◽  
Global Pandemic ◽  
Actual Growth ◽  
Global Threat ◽  
The Impact

AbstractCOVID-19 has become a global pandemic. However, the impact of the public health interventions in China needs to be evaluated. We established a SEIRD model to simulate the transmission trend of China. In addition, the reduction of the reproductive number was estimated under the current forty public health interventions policies. Furthermore, the infection curve, daily transmission replication curve, and the trend of cumulative confirmed cases were used to evaluate the effects of the public health interventions. Our results showed that the SEIRD curve model we established had a good fit and the basic reproductive number is 3.38 (95% CI, 3.25–3.48). The SEIRD curve show a small difference between the simulated number of cases and the actual number; the correlation index (H2) is 0.934, and the reproductive number (R) has been reduced from 3.38 to 0.5 under the current forty public health interventions policies of China. The actual growth curve of new cases, the virus infection curve, and the daily transmission replication curve were significantly going down under the current public health interventions. Our results suggest that the current public health interventions of China are effective and should be maintained until COVID-19 is no longer considered a global threat.

scholarly journals Worldwide Effectiveness of Various Non-Pharmaceutical Intervention Control Strategies on the Global COVID-19 Pandemic: A Linearised Control Model

2020 ◽  
Author(s):  
Jacques Naude ◽  
Bruce Mellado ◽  
Joshua Choma ◽  
Fabio Correa ◽  
Salah Dahbi ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Control Strategies ◽  
Kernel Functions ◽  
Model Parameters ◽  
Government Response ◽  
Specific Effects ◽  
Global Pandemic ◽  
Adjustment Time ◽  
The Us ◽  
Clinical Advances

Background COVID-19 is a virus which has lead to a global pandemic. Worldwide, more than 130 countries have imposed severe restrictions, which form part of a set of non-pharmaceutical interventions (NPI)s. We aimed to quantify the country-specific effects of these NPIs and compare them using the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index, p, as a measure of NPI stringency. Methods We developed a dual latent/observable Susceptible Infected Recovered Deaths (SIRD) model and applied it on each of 22 countries and 25 states in the US using publicly available data. The observable model parameters were extracted using kernel functions. The regression of the transmission rate, β, as a function of p in each locale was modeled through the intervention leverage, αs, an initial transmission rate, β0 and a typical adjustment time, br-1. Results The world average for the intervention leverage, αs=0.01 (95% CI 0.0102 - 0.0112) had an ensemble standard deviation of 0.0017 (95% C.I. 0.0014 - 0.0021), strongly indicating a universal behavior. Discussion Our study indicates that removing NPIs too swiftly will result in the resurgence of the spread within one to two months, in alignment with the current WHO recommendations. Moreover, we have quantified and are able to predict the effect of various combinations of NPIs. There is a minimum NPI level, below which leads to resurgence of the outbreak (in the absence of pharmaceutical and clinical advances). For the epidemic to remain sub-critical, the rate with which the intervention leverage αs increases should outpace that of the relaxation of NPIs.

Campylobacter Species: Considerations For Controlling A Foodborne Pathogen

1988 ◽  
Vol 51 (2) ◽  
pp. 145-153 ◽  
Author(s):  
DON A. FRANCO
Keyword(s):  
Foodborne Pathogen ◽  
Wide Distribution ◽  
Convincing Evidence ◽  
Animal Origin ◽  
Food Protection ◽  
Control And Prevention ◽  
Sources Of Infection ◽  
And Storage ◽  
Animal Reservoirs

Studies in the past decade have demonstrated with convincing evidence that Campylobacter jejuni is an important enteric pathogen of man. The wide distribution of the organism in animal reservoirs, and in foods of animal origin makes control of this foodborne microbe a formidable undertaking. Although the vehicles that are incriminated as sources of infection are broad, most illnesses occur sporadically without a finite determination as to the mode of transmission. The problem is further amplified because an infectious zoonotic disease like Campylobacter enteritis not only occurs frequently, but is almost always unsuspected, and too often unrecognized. Factors that perpetuate the Campylobacter problem are spreading Campylobacter during animal slaughtering and processing, concentrating animals in feedlots and brooding houses, poor food handling and storage practices, environmental contamination from animal wastes and other sources. Campylobacteriosis is a universal problem and an immense challenge to all who work in the arena of food protection. The solutions for control and prevention are demanding. In addition to more needed research, close national and international cooperation is a mandate if progress will be realized in the long-term minimization, and eventual elimination of this pathogen.

scholarly journals How Containment Can Effectively Suppress the Outbreak of COVID-19: A Mathematical Modeling

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 204
Author(s):  
Bootan Rahman ◽  
Sarbaz H. A. Khoshnaw ◽  
Grace O. Agaba ◽  
Fahad Al Basir
Keyword(s):  
Transmission Rate ◽  
Model Parameters ◽  
Infection Transmission ◽  
Numerical Investigations ◽  
Significant Parameter ◽  
Global Pandemic ◽  
Sensitivity Indices ◽  
Proposed Model ◽  
Reproduction Numbers ◽  
Parsimonious Model

In this paper, the aim is to capture the global pandemic of COVID-19 with parameters that consider the interactions among individuals by proposing a mathematical model. The introduction of a parsimonious model captures both the isolation of symptomatic infected individuals and population lockdown practices in response to containment policies. Local stability and basic reproduction numbers are analyzed. Local sensitivity indices of the parameters of the proposed model are calculated, using the non-normalization, half-normalization, and full-normalization techniques. Numerical investigations show that the dynamics of the system depend on the model parameters. The infection transmission rate (as a function of the lockdown parameter) for both reported and unreported symptomatic infected peoples is a significant parameter in spreading the infection. A nationwide public lockdown decreases the number of infected cases and stops the pandemic’s peak from occurring. The results obtained from this study are beneficial worldwide for developing different COVID-19 management programs.

scholarly journals Journey so far with COVID 19 – a comprehensive review

2021 ◽  
Vol 18 (4) ◽  
pp. 303-317
Author(s):  
Sayak Roy ◽  
Keyword(s):  
Controlled Trial ◽  
Severe Disease ◽  
Clinical Manifestations ◽  
Treatment Modalities ◽  
Comprehensive Review ◽  
Modes Of Transmission ◽  
Randomized Controlled ◽  
Global Pandemic ◽  
Disease Condition ◽  
Strict Protocol

Introduction. The occurrence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has emerged as a global pandemic with huge death tolls. Coronavirus disease 2019 (COVID-19) may progress from minimal infection to serious respiratory failure mandating treatment for a continuum of developed disease condition. Aim. The purpose of this review is to summarize the findings related to epidemiology, clinical manifestations, modes of transmission, diagnosis and the treatment modalities (both experimental and repurposed) for COVID-19. Material and methods. Literature were searched using various search engines like PubMed, SCOPUS, EMBASE, J-Gate, Google Scholar to look for review articles, randomized controlled trial results, prospective studies and, retrospective studies done on COVID-19 for the purpose of this comprehensive review. Analysis of the literature. The transmission seems to be occurring through droplet, fomite and aerosols (rarely). Currently there is no specific/targeted vaccine available. Priority is highly placed to identify possible treatment approaches to circumvention this disease. Conclusion. Till we find a vaccine, we have to strategize to optimally use the existing evidence of the indirect effects of these various available drugs for therapy and maintain a strict protocol for prevention and we must use triage system to admit only those critically ill or having severe disease.

scholarly journals Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

2021 ◽  
Author(s):  
Zhi Wen ◽  
Guido Powell ◽  
Imane Chafi ◽  
David Buckeridge ◽  
Yue Li
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Global Scale ◽  
Health Interventions ◽  
Future Research ◽  
Public Health Interventions ◽  
News Reports ◽  
Country Level ◽  
Global Pandemic ◽  
Latent Topics

The COVID-19 global pandemic has highlighted the importance of non-pharmacological interventions (NPI) for controlling epidemics of emerging infectious diseases. Despite the importance of NPI, their implementation has been monitored in an ad hoc and uncoordinated manner, mainly through the manual efforts of volunteers. Given the absence of systematic NPI tracking, authorities and researchers are limited in their ability to quantify the effectiveness of NPI and guide decisions regarding their use during the progression of a global pandemic. To address this issue, we propose 3-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI by mining the vast amount of unlabelled news reports about these interventions. Building on topic modeling, our method characterizes online government reports and media articles related to COVID-19 as a mixture of latent topics. Our key contribution is the use of transfer-learning to address the limited number of NPI-labelled documents and topic modelling to support interpretation of the results. At stage 1, we trained a modified version of the unsupervised dynamic embedded topic model (DETM) on 1.2 million international news reports related to COVID-19. At stage 2, we used the trained DETM to infer topic mixture from a small set of 2000 NPI-labelled WHO documents as the input features for predicting NPI labels on each document. At stage 3, we supply the inferred country-level temporal topics from the DETM to the pretrained document-level NPI classifier to predict country-level NPIs. We identified 25 interpretable topics, over 4 distinct and coherent COVID-related themes. These topics contributed to significant improvements in predicting the NPIs labelled in the WHO documents and in predicting country-level NPIs. Together, our work lay the machine learning methodological foundation for future research in global-scale surveillance of public health interventions. The EpiTopics code is available at GitHub: https://github.com/li-lab-mcgill/covid-npi.

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