scholarly journals COVID-19: Short term prediction model using daily incidence data

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250110
Author(s):  
Hongwei Zhao ◽  
Naveed N. Merchant ◽  
Alyssa McNulty ◽  
Tiffany A. Radcliff ◽  
Murray J. Cote ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Health Planning ◽  
Time Interval ◽  
Short Time Interval ◽  
State Planning ◽  
Incidence Data ◽  
Short Term Prediction ◽  
Incident Cases

Background Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. Results We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. Conclusion We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

scholarly journals COVID-19: Short term prediction model using daily incidence data

2020 ◽  
Author(s):  
Hongwei Zhao ◽  
Naveed N Merchant ◽  
Alyssa McNulty ◽  
Tiffany A Radcliff ◽  
Murray J Cote ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Health Planning ◽  
Time Interval ◽  
Short Time Interval ◽  
State Planning ◽  
Incidence Data ◽  
Short Term Prediction ◽  
Incident Cases

AbstractBackgroundPrediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions.MethodsOur approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree.ResultsWe apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time.ConclusionWe presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

scholarly journals Definition and estimation of an actual reproduction number describing past infectious disease transmission: application to HIV epidemics among homosexual men in Denmark, Norway and Sweden

2004 ◽  
Vol 132 (6) ◽  
pp. 1139-1149 ◽  
Author(s):  
E. J. AMUNDSEN ◽  
H. STIGUM ◽  
J.-A. RØTTINGEN ◽  
O. O. AALEN
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Infectious Period ◽  
Secondary Case ◽  
Time Interval ◽  
Homosexual Men ◽  
Infectious Disease Transmission ◽  
A Value ◽  
The Common

Prevalence and incidence measures are the common way to describe epidemics. The reproduction number supplies information on the potential for growth or decline of an epidemic. We define an actual reproduction number for infectious disease transmission that has taken place. An estimator is suggested, based on the number of new infections observed in a given time-interval, the number of those infected at the start of the interval, and the length of the infectious period. That estimator is applied to HIV among men having sex with other men over the period, 1977–1995, in Scandinavia. The actual reproduction number was estimated with acceptable certainty from the period, 1981–1982, yielding a value of 15 secondary cases. A value of less than one secondary case was assessed for the period, 1988–1995, in Denmark and Sweden. The actual reproduction number gives us some additional understanding of the dynamics of epidemics, compared with prevalence and incidence curves.

scholarly journals Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

2020 ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Region Of Interest ◽  
Health Interventions ◽  
Transmission Model ◽  
Public Health Interventions ◽  
Containment Measures ◽  
The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

scholarly journals The Effect of Seasonal Weather Variation on the Dynamics of the Plague Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-25 ◽  
Author(s):  
Rigobert C. Ngeleja ◽  
Livingstone S. Luboobi ◽  
Yaw Nkansah-Gyekye
Keyword(s):  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Effective Control ◽  
Human Beings ◽  
Weather Variation ◽  
The Basic Reproduction Number ◽  
Seasonal Weather

Plague is a historic disease which is also known to be the most devastating disease that ever occurred in human history, caused by gram-negative bacteria known as Yersinia pestis. The disease is mostly affected by variations of weather conditions as it disturbs the normal behavior of main plague disease transmission agents, namely, human beings, rodents, fleas, and pathogens, in the environment. This in turn changes the way they interact with each other and ultimately leads to a periodic transmission of plague disease. In this paper, we formulate a periodic epidemic model system by incorporating seasonal transmission rate in order to study the effect of seasonal weather variation on the dynamics of plague disease. We compute the basic reproduction number of a proposed model. We then use numerical simulation to illustrate the effect of different weather dependent parameters on the basic reproduction number. We are able to deduce that infection rate, progression rates from primary forms of plague disease to more severe forms of plague disease, and the infectious flea abundance affect, to a large extent, the number of bubonic, septicemic, and pneumonic plague infective agents. We recommend that it is more reasonable to consider these factors that have been shown to have a significant effect on RT for effective control strategies.

scholarly journals Assessing the Potential Impact of Immunity Waning on the Dynamics of COVID-19: An Endemic Model of COVID-19

2021 ◽  
Author(s):  
MUSA RABIU ◽  
Sarafa A. Iyaniwura
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Backward Bifurcation ◽  
Disease Dynamics ◽  
Potential Impact ◽  
The Impact ◽  
Induced Immunity ◽  
Disease Free ◽  
Pharmaceutical Interventions

Abstract We developed an endemic model of COVID-19 to assess the impact of vaccination and immunity waning on the dynamics of the disease. Our model exhibits the phenomenon of backward bifurcation and bi-stability, where a stable disease-free equilibrium co-exists with a stable endemic equilibrium. The epidemiological implication of this is that the control reproduction number being less than unity is no longer sufficient to guarantee disease eradication. We showed that this phenomenon could be eliminated by either increasing the vaccine efficacy or by reducing the disease transmission rate (adhering to non-pharmaceutical interventions). Furthermore, we numerically investigated the impacts of vaccination and waning of both vaccine-induced immunity and post-recovery immunity on the disease dynamics. Our simulation results show that the waning of vaccine-induced immunity has more effect on the disease dynamics relative to post-recovery immunity waning, and suggests that more emphasis should be on reducing the waning of vaccine-induced immunity to eradicate COVID-19.

Dynamical Behavior of an Epidemic Model in a Fuzzy Transmission

2015 ◽  
Vol 23 (05) ◽  
pp. 651-665 ◽  
Author(s):  
Prasanta Kumar Mondal ◽  
Soovoojeet Jana ◽  
Palash Haldar ◽  
T. K. Kar
Keyword(s):  
Epidemic Model ◽  
Disease Transmission ◽  
Fuzzy Numbers ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Dynamical Behavior ◽  
Threshold Condition ◽  
Treatment Control ◽  
Fuzzy Expected Value ◽  
Treatment Function

In this paper, we have formulated a simple SIS type epidemic model in the presence of treatment control, and we have discussed the dynamical behavior of the system. The system is modified by considering both the disease transmission rate and the treatment function as fuzzy numbers, and also the fuzzy expected value of the infected individuals is calculated. Furthermore, the fuzzy basic reproduction number is investigated and a threshold condition of pathogen is obtained at which the system undergoes a transcritical bifurcation.

A MATHEMATICAL ANALYSIS OF ZIKA VIRUS EPIDEMIC IN RIO DE JANEIRO AS A VECTOR-BORNE AND SEXUALLY TRANSMITTED DISEASE

2019 ◽  
Vol 27 (01) ◽  
pp. 83-105 ◽  
Author(s):  
GUSTAVO CRUZ-PACHECO ◽  
LOURDES ESTEVA ◽  
CLAUDIA PIO FERREIRA
Keyword(s):  
Basic Reproduction Number ◽  
Zika Virus ◽  
Disease Transmission ◽  
Rio De Janeiro ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Sexual Transmission ◽  
Transmitted Disease ◽  
Infectious Period ◽  
Sexually Transmitted

In this work we formulate a mathematical model to assess the importance of sexual transmission during the Zika virus outbreak that occurred in Rio de Janeiro, Brazil, in 2015. To this end, we deduce from the model an analytical expression of the basic reproduction number of Zika, [Formula: see text], in terms of the vectorial and sexual transmissions, and we use the estimations given in Ref. 1 [Villela DAM, Bastos LS, de Carvalho LM, Cruz OG, Gomes MFC, Durovni B, Lemos MC, Saraceni V, Coelho FC, Codeço CT, Zika in Rio de Janeiro: Assessment of basic reproduction number and comparison with dengue outbreaks, Epidemiol Infect 145(8):1649–1657, 2017] for the [Formula: see text] values of Zika virus and dengue virus epidemics in Rio de Janeiro to evaluate the contribution of sexual transmission of Zika virus. According to the obtained results, sexual transmission (pure plus mediated by vector transmission) contributes from 23% to 46% for the [Formula: see text] increment. Also, an asymmetric sexual transmission between men and women can explain the fact that the incidence of Zika virus in women was 60% higher than in man during the 2015 epidemics. We also carry out a sensitivity analysis using [Formula: see text] as the output parameter. The results of this analysis have shown that the transmission rate between human and mosquito populations, the mosquito mortality rate, and the human infectious period are the parameters that contribute more to the [Formula: see text] variation, highlighting the importance of vector control to halt disease transmission.

scholarly journals Tracing DAY-ZERO and Forecasting the Fade out of the COVID-19 Outbreak in Lombardy, Italy: A Compartmental Modelling and Numerical Optimization Approach

2020 ◽  
Author(s):  
Lucia Russo ◽  
Cleo Anastassopoulou ◽  
Athanasios Tsakris ◽  
Gennaro Nicola Bifulco ◽  
Emilio Fortunato Campana ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Disease Transmission ◽  
Total Population ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Cumulative Number ◽  
Community Mobility ◽  
The Basic Reproduction Number

AbstractItaly currently constitutes the epicenter of the novel coronavirus disease (COVID-19) pandemic, having surpassed China’s death toll. The disease is sweeping through Lombardy, which remains in lockdown since the 8th of March. As of the same day, the isolation measures taken in Lombardy have been extended to the entire country. Here, we provide estimates for: (a) the DAY-ZERO of the outbreak in Lombardy, Italy; (b) the actual number of exposed/infected cases in the total population; (c) the basic reproduction number (R0); (d) the “effective” per-day disease transmission; and, importantly, (e) a forecast for the fade out of the outbreak, on the basis of the COVID-19 Community Mobility Reports released by Google on March 29.MethodsTo deal with the uncertainty in the number of actual exposed/ infected cases in the total population, we address a compartmental Susceptible/ Exposed/ Infectious/ Recovered/ Dead (SEIRD) model with two compartments of infectious persons: one modelling the total cases in the population and another modelling the confirmed cases. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death, the case fatality ratio, and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-For the estimation of the DAY-ZERO of the outbreak in Lombardy, as well as of the “effective” per-day transmission rate for which no clinical data are available, we have used the SEIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March, the lockdown day of Lombardy and of all Italy. This was accomplished by solving a mixed-integer optimization problem with the aid of genetic algorithms. Based on the computed values, we also provide an estimation of the basic reproduction number R0. Furthermore, based on an estimation for the reduction in the “effective” transmission rate of the disease as of March 8 that reflects the suspension of almost all activities in Italy, we ran the simulator to forecast the fade out of the epidemic. For this purpose, we considered the reduction in mobility in Lombardy as released on March 29 by Google COVID-19 Community Mobility Reports, the effect of social distancing, and the draconian measures taken by the government on March 20 and March 21, 2020.ResultsBased on the proposed methodological procedure, we estimated that the DAY-ZERO was most likely between January 5 and January 23 with the most probable date the 15th of January 2020. The actual cumulative number of exposed cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases. The “effective” per-day disease transmission rate for the period until March 8 was found to be 0.686 (95% CI:0.660, 0.713), while the basic reproduction number R0 was found to be 4.51 (95% CI: 4.14, 4.90).Importantly, simulations show that the COVID-19 pandemic in Lombardy is expected to fade out by the end of May -early June, 2020, if the draconian, as of March 20 and March 21, measures are maintained.

scholarly journals Mathematical Modelling For Decision Making of Lockdown during COVID-19

2020 ◽  
Author(s):  
Ahona Ghosh ◽  
Sandip Roy ◽  
Suparna Biswas
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Virus Transmission ◽  
Business Travel ◽  
Travel And Tourism ◽  
Quick Recovery ◽  
Experimental Findings ◽  
Tourism Economy ◽  
Education Business

Abstract Due to the recent worldwide outbreak of COVID-19, there has been a huge change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel and tourism, economy in all the affected countries. In this scenario, people have to be careful and cautious about the symptoms and should act accordingly. Accurate predictions of factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the situation and precautions should be taken wisely. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level and concepts like recovery trend analysis in the developed and developing countries have not been discussed ever. The novelty of our proposed work lies here. In this paper, we have analysed the nature of spreading of the said disease using Time Dependent Discrete Susceptible Infected Recovered (TDDSIR) model on the data collected from various platforms and then, fifteen countries from first, second and third world have been considered to have an idea of probable future projections of pandemic. Experimental findings proved that proper social distancing measures during lockdown has been a controller of the disease transmission trend as the basic reproduction number, being actually the transmission rate and not the number of infectives, decreases with the strict lockdown decisions made by different countries. However, people should be more aware of the consequences for quick recovery from the various obstacles of the current situation.

scholarly journals An investigation of spatial-temporal patterns and predictions of the COVID-19 pandemic in Colombia, 2020-2021

2021 ◽  
Author(s):  
Amna Tariq ◽  
Tsira Chakhaia ◽  
Sushma Dahal ◽  
Alexander Ewing ◽  
Xinyi Hua ◽  
...  
Keyword(s):  
Social Media ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Growth Dynamics ◽  
Short Term ◽  
Transmission Potential ◽  
Incidence Data ◽  
Richards Model ◽  
First Case ◽  
Reproduction Numbers

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 4,240,982 cases and 106,544 deaths as of June 30, 2021. This motivates an investigation of the SARS-CoV-2 transmission dynamics at the national and regional level using case incidence data. Mathematical models are employed to estimate the transmission potential and perform short-term forecasts of the COVID-19 epidemic trajectory in Colombia. Furthermore, geographic heterogeneity of COVID-19 in Colombia is examined along with the analysis of mobility and social media trends, showing that the increase in mobility in July 2020 and January 2021 were correlated with surges in case incidence. The estimation of national and regional reproduction numbers shows sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Moreover, most recent estimates of reproduction number are >1.0 at the national and regional levels as of May 30, 2021. Further, the 30-day ahead short-term forecasts obtained from Richards model present a sustained decline in case counts in contrast to the sub-epidemic and GLM model. Nevertheless, our spatial analysis in Colombia shows distinct variations in incidence rate patterns across different departments that can be grouped into four distinct clusters. Lastly, the correlation of social media trends and adherence to social distancing measures is observed by the fact that a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued.

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