Mathematical modeling for infectious viral disease: The COVID‐19 perspective

Adekola, Hafeez Aderinsayo; Adekunle, Ibrahim Ayoade; Egberongbe, H. O.; Onitilo, S. A.; Abdullahi, Idris Nasir

doi:10.1002/pa.2306

Cited by 33 publications

(24 citation statements)

References 30 publications

(61 reference statements)

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“…Mathematical modelling is an effective tool in this situation because, in the absence of direct data on the risk of rare events, models can synthesise data on underlying aspects of the process (e.g. transmission dynamics, virus incubation period, surveillance programs, and false negative testing rates) to quantify this risk (12). We use a simple mathematical model of COVID-19 transmission and testing to investigate the effect of different border policy settings on the risk of a community outbreak seeded at the border via each of these routes.…”

Section: Introductionmentioning

confidence: 99%

Managing the risk of a COVID-19 outbreak from border arrivals

Steyn

Plank

James

et al. 2020

Preprint

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In an attempt to maintain elimination of COVID-19, the New Zealand government has closed the border to everyone except citizens and residents. All arrivals are required to spend 14 days in government-managed isolation/quarantine and to be tested for COVID-19 on day 3 and on day 12 of their stay. We model the testing, isolation and potential transmission of COVID-19 within managed isolation facilities to estimate the risk of undetected cases and the risk of infectious cases being released into the community. We use a stochastic individual-based that includes a time-dependent probability of a false negative test result, complete isolation of confirmed and probable cases, and secondary transmission of COVID-19 between close contacts. We show that the combination of 14-day quarantine with day 3 and day 12 testing reduces risk of releasing an infectious case to around 0.1% per infected arrival. Shorter quarantine periods, or reliance on testing only with no quarantine, substantially increase this risk. It is important to avoid contacts between individuals staying in quarantine to minimise the risk of secondary transmission. We calculate the ratio of cases detected on day 3 to cases detected on day 12 in the model and show that this may be a useful indicator of the likelihood of secondary transmission occurring within quarantine. We do not explicitly model transmission of COVID-19 from individuals in quarantine to staff, but this is likely to present a significant risk. This needs to be minimised by strict infection control, use of personal protective equipment by staff at all times, and avoiding close contact between staff and hotel guests.

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Section: Introductionmentioning

confidence: 99%

Managing the risk of a COVID-19 outbreak from border arrivals

Steyn

Plank

James

et al. 2020

Preprint

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“…Researchers, stakeholders, government, and society are actively looking for ways to reduce the rate of infection until it is cured or to establish a vaccination course. [23] SHS Web of Conferences 92, 010 (2021) Globalization and its Socio-Economic Consequences 2020…”

Section: Resultsmentioning

confidence: 99%

Impact of COVID 19 on the provision of services by public administration

Zilincikova

Štofková

2021

SHS Web of Conf.

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Research background: The digitization of public administration processes has helped the functioning of the public sector at a time when the health of citizens is threatened by the virus. The economic sustainability of public administration has its place in government programs as an important means of modernizing, supporting, and optimizing the functioning of the public sector, improving public services. It focuses on a sustainable state in an emergency, or in times of crisis. Purpose of the article: The aim of the paper is to analyze and compare electronic services provided by public administration to citizens, businesses before and after the corona virus. The above analysis was supplemented by a questionnaire survey among citizens, entrepreneurs of the Slovak Republic focusing on the satisfaction of electronic public administration services at the time of the corona virus. Methods: The article applied the analysis and comparison of the state of provided electronic services before and after the corona virus. Prediction of future development was determined based on available data, by ex-ante analysis using the method of simple moving averages. An additional method of research was primary research satisfaction. Findings & Value added: An important milestone in the time of the coronary virus pandemic is the optimization of systems, services, and the strengthening of intervention capacities for emergency management. The findings presented in the paper point to changes related to the economic development of the state economy, changes in employment in the labor market and planned measures to rehabilitate the damage caused by COVID 19.

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“…As the number of cases increases and more data becomes available, various researches [1] , [2] , [3] , [4] , [5] , [6] , [7] develop a range of mathematical models or employ machine learning algorithms to forecast the transmission of SARS-CoV-2. Previous studies have also employed LSTM [8] , [9] , [10] , [11] , [12] or XGBoost [13] , [14] , [15] , [16] , [17] , [18] , [19] models to forecast the spread of COVID-19 and identify the most influential COVID-19 indicators.…”

Section: Introductionmentioning

confidence: 99%

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

Luo

Zhang

et al. 2021

Results in Physics

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In this paper, we establish daily confirmed infected cases prediction models for the time series data of America by applying both the long short-term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms, and employ four performance parameters as MAE, MSE, RMSE, and MAPE to evaluate the effect of model fitting. LSTM is applied to reliably estimate accuracy due to the long-term attribute and diversity of COVID-19 epidemic data. Using XGBoost model, we conduct a sensitivity analysis to determine the robustness of predictive model to parameter features. Our results reveal that achieving a reduction in the contact rate between susceptible and infected individuals by isolated the uninfected individuals, can effectively reduce the number of daily confirmed cases. By combining the restrictive social distancing and contact tracing, the elimination of ongoing COVID-19 pandemic is possible. Our predictions are based on real time series data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.

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Mathematical modeling for infectious viral disease: The COVID‐19 perspective

Cited by 33 publications

References 30 publications

Managing the risk of a COVID-19 outbreak from border arrivals

Managing the risk of a COVID-19 outbreak from border arrivals

Impact of COVID 19 on the provision of services by public administration

Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

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