scholarly journals Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

2021 ◽  
Vol 26 (1) ◽  
pp. 16
Author(s):  
Christopher A. Cullenbine ◽  
Joseph W. Rohrer ◽  
Erin A. Almand ◽  
J. Jordan Steel ◽  
Matthew T. Davis ◽  
...  

A closed-form equation, the Fizzle Equation, was derived from a mathematical model predicting Severe Acute Respiratory Virus-2 dynamics, optimized for a 4000-student university cohort. This equation sought to determine the frequency and percentage of random surveillance testing required to prevent an outbreak, enabling an institution to develop scientifically sound public health policies to bring the effective reproduction number of the virus below one, halting virus progression. Model permutations evaluated the potential spread of the virus based on the level of random surveillance testing, increased viral infectivity and implementing additional safety measures. The model outcomes included: required level of surveillance testing, the number of infected individuals, and the number of quarantined individuals. Using the derived equations, this study illustrates expected infection load and how testing policy can prevent outbreaks in an institution. Furthermore, this process is iterative, making it possible to develop responsive policies scaling the amount of surveillance testing based on prior testing results, further conserving resources.

2021 ◽  
Author(s):  
Sarafa A. Iyaniwura ◽  
Musa Rabiu ◽  
Jummy F. David ◽  
Jude D. Kong

AbstractAdherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that direct transmission has more effect on the reproduction number and final epidemic size, relative to indirect transmission. In addition, we showed that there is a significant benefit in adhering to the COVID-19 NPIs.


Author(s):  
Ulrich KAMGUEM NGUEMDJO ◽  
Freeman MENO ◽  
Audric DONGFACK ◽  
Bruno VENTELOU

This paper analyses the evolution of COVID 19 disease in Cameroon over the period March 6 April 2020 using SIR model. Specifically, 1) we evaluate the basic reproduction number of the virus. 2) Determine the peak of the infection and the spread-out period of the disease. 3) Simulate the interventions of public health authorities. Data used in this study is obtained from the Ministry of Health of Cameroon. The results suggest that over the period, the reproduction number of the COVID 19 in Cameroon is about 1.5 and the peak of the infection could occur at the end of May 2020 with about 7.7% of the population infected. Besides, implementation of efficient public health policies could help flattens the epidemic curve.


PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249456
Author(s):  
Brydon Eastman ◽  
Cameron Meaney ◽  
Michelle Przedborski ◽  
Mohammad Kohandel

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.


JAMIA Open ◽  
2021 ◽  
Author(s):  
Bo Peng ◽  
Rowland W Pettit ◽  
Christopher I Amos

Abstract Objectives We developed COVID-19 Outbreak Simulator (https://ictr.github.io/covid19-outbreak-simulator/) to quantitatively estimate the effectiveness of preventative and interventive measures to prevent and battle COVID-19 outbreaks for specific populations. Materials and methods Our simulator simulates the entire course of infection and transmission of the virus among individuals in heterogeneous populations, subject to operations and influences, such as quarantine, testing, social distancing, and community infection. It provides command-line and Jupyter notebook interfaces and a plugin system for user-defined operations. Results The simulator provides quantitative estimates for COVID-19 outbreaks in a variety of scenarios and assists the development of public health policies, risk-reduction operations, and emergency response plans. Discussion Our simulator is powerful, flexible, and customizable, although successful applications require realistic estimation and robustness analysis of population-specific parameters. Conclusion Risk assessment and continuity planning for COVID-19 outbreaks are crucial for the continued operation of many organizations. Our simulator will be continuously expanded to meet this need.


2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R S Caló ◽  
B S N Souza ◽  
N D Galvão ◽  
R A G Souza ◽  
J C S Oliveira ◽  
...  

Abstract Background Colorectal cancer has been one of the cancers that most contributed to mortality, in both sexes in the world. In Brazil, cancer is among the top five causes of death and colorectal cancer is ranked on the fifth position. Of the Federative Units belonging to the Legal Amazon, Mato Grosso stands out for the higher adjusted incidence of colorectal cancer for both sexes. Thus, the objective is to characterize deaths from colorectal cancer, according to sociodemographic variables in Mato Grosso from 2000 to 2016. Methods A descriptive study was carried out, using data from the Mortality Information System, made available by the Department of Health of the Mato Grosso State. Deaths of all ages were selected, whose basic cause was identified by the codes from the International Classification of Diseases: (C.18) colon cancer, (C.19) rectosigmoid junction cancer, (C.20) rectal cancer or (C.21) anus cancer. Results Between 2000 and 2016, 31,607 deaths from cancer were registered. Of these, 1,750 (5.6%) were due to colorectal cancer. An increased number of deaths was observed at the end of the period, with a variation from 46 deaths in 2000 from 173 in 2016. Highest frequency was verified in men (51.3%), people aged 60 years or older (59.7%), black (54.6%), married (52.3%) and those with primary education (55.2%). According to Brazilian occupation classification options or those answers filled out on the death certificate, highest frequency were for “Retired” (26.2%), “Housewife” (23.1%), Agricultural/Forestry and Fisheries” (11.3%) and “Production of Industrial Goods and Services” (10.3%). Conclusions This study evidenced the increased number of deaths due to colorectal cancer in Mato Grosso State, and identified priority groups for interventions through public health policies which should include screening and early diagnosis to cope with the disease. Key messages Evidenced the increased number of deaths due to colorectal cancer in Mato Grosso State. Identified priority groups for interventions through public health policies.


2021 ◽  
Vol 17 (2) ◽  
pp. 186-203
Author(s):  
Nathan Genicot

AbstractThe COVID-19 pandemic has given rise to the massive development and use of health indicators. Drawing on the history of international public health and of the management of infectious disease, this paper attempts to show that the normative power acquired by metrics during the pandemic can be understood in light of two rationales: epidemiological surveillance and performance assessment. On the one hand, indicators are established to evaluate and rank countries’ responses to the outbreak; on the other, the evolution of indicators has a direct influence on the content of public health policies. Although quantitative data are an absolute necessity for coping with such disasters, it is critical to bear in mind the inherent partiality and precarity of the information provided by health indicators. Given the growing importance of normative quantitative devices during the pandemic, and assuming that their influence is unlikely to decrease in the future, they call for close scrutiny.


The Lancet ◽  
2017 ◽  
Vol 390 ◽  
pp. S12 ◽  
Author(s):  
Katie Thomson ◽  
Frances Hillier-Brown ◽  
Adam Todd ◽  
Courtney McNamara ◽  
Tim Huijits ◽  
...  

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