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1.
J Biol Dyn ; 16(1): 619-639, 2022 12.
Article in English | MEDLINE | ID: covidwho-2187649

ABSTRACT

In this paper, we are concerned with an epidemic model with quarantine and distributed time delay. We define the basic reproduction number R0 and show that if R0≤1, then the disease-free equilibrium is globally asymptotically stable, whereas if R0>1, then it is unstable and there exists a unique endemic equilibrium. We obtain sufficient conditions for a Hopf bifurcation that induces a nontrivial periodic solution which represents recurrent epidemic waves. By numerical simulations, we illustrate stability and instability parameter regions. Our results suggest that the quarantine and time delay play important roles in the occurrence of recurrent epidemic waves.


Subject(s)
Epidemics , Quarantine , Basic Reproduction Number , Computer Simulation , Models, Biological
2.
Rev. bras. promoç. saúde (Impr.) ; 34(1): 1-9, 17/02/2021.
Article in Spanish | WHO COVID, LILACS (Americas) | ID: covidwho-2202501

ABSTRACT

Objetivo: Este artículo de investigación busca conocer la influencia de la propagación del virus COVID-19 a través de la temperatura y de la humedad en España y Brasil. Métodos: Para el cálculo de la variación mensual del índice de propagación del virus COVID-19 por provincias en España se han utilizado, en primer lugar, las series climáticas de la AEMET de España e INMETRO de Brasil. Se han extraído las medias correspondientes y después se han sometido los datos a un proceso de homogenización, para posteriormente poder calcular el incremento mensual de temperatura y de humedad por provincias y estados. Este proceso metodológico establece una relación directamente proporcional entre el aumento de la temperatura y de la humedad con el índice de propagación del virus COVID-19. Resultados: En España, las condiciones climáticas favorecerán la disminución o aumento del índice reproductivo del virus. En Brasil las condiciones climáticas no favorecerán la disminución del índice reproductivo del virus y, climatológicamente, no existe un periodo óptimo para una desescalada y vuelta a la normalidad. Las variaciones de las condiciones climáticas en Brasil no son significativas, por lo que el clima de Brasil no influye en la disminución de propagación del virus. Conclusión: El clima influye en la propagación del virus. Descriptores: COVID-19; Transmisión de Enfermedad Infecciosa; Clima; Temperatura; Humedad.


Objetivo: Este artigo de pesquisa busca conhecer a influência da propagação do vírus COVID-19 através da temperatura e umidade na Espanha e no Brasil. Métodos: Para calcular a variação mensal do índice de propagação do vírus COVID-19 por províncias da Espanha, primeiramente, utilzaram-se as séries climáticas da AEMET da Espanha e do INMETRO do Brasil. Extraíram-se as médias correspondentes, para posterior submissão dos dados a um processo de homogeneização, com o intuito de calcular o aumento mensal de temperatura e umidade por províncias e estados. Esse processo metodológico estabeleceu uma relação diretamente proporcional entre o aumento da temperatura e da umidade com a taxa de disseminação do vírus COVID-19. Resultados: Na Espanha, as condições climáticas favoreceram a diminuição ou aumento do índice reprodutivo do vírus. No Brasil, entretanto, as condições climáticas não favorecem a diminuição do índice reprodutivo do virus, comprovando que climatologicamente não existe um período ideal para uma desaceleração e retorno à normalidade. As variações nas condições climáticas no Brasil não são significativas, portanto o clima não influencia na diminuição da propagação do vírus neste país. Conclusão: O clima influencia a disseminação do vírus. Descritores: COVID-19; Transmissão de Doença Infecciosa; Clima; Temperatura; Umidade.


Objective: This research article seeks to know the influence of the spread of the COVID-19 virus through temperature and humidity in Spain and Brazil. Methods: In order to calculate the monthly variation in the COVID-19 virus spread index by provinces in Spain, at first, the climatic series of the AEMET of Spain and INMETRO of Brazil were used. The corresponding means have been extracted and then the data have been subjected to a homogenization process, to later be able to calculate the monthly increase in temperature and humidity by provinces and states. This methodological process establishes a directly proportional the climatic conditions favored the decrease or increase of the reproductive index of the virus. In Brazil, however, the climatic conditions do not favor the decrease in the reproductive index of the virus, proving that climatologically there is no optimal period for de-escalation and return to normality. The variations in climatic conditions in Brazil are not significant, so the climate does not influence the decrease in the spread of the virus. Conclusion: Climate influences the spread of the virus. Descriptors: COVID-19; Disease Transmission, Infectious; Climate; Temperature; Humidity. relationship between the increase in temperature and humidity with the spread rate of the COVID-19 virus. Results: In Spain the climatic conditions favored the decrease or increase of the reproductive index of the virus. In Brazil, however, the climatic conditions do not favor the decrease in the reproductive index of the virus, proving that climatologically there is no optimal period for de-escalation and return to normality. The variations in climatic conditions in Brazil are not significant, so the climate does not influence the decrease in the spread of the virus. Conclusion: Climate influences the spread of the virus.


Subject(s)
Temperature , Disease Transmission, Infectious , Basic Reproduction Number , COVID-19 , Humidity
3.
Medicine (Baltimore) ; 100(18): e25837, 2021 May 07.
Article in English | MEDLINE | ID: covidwho-2191001

ABSTRACT

BACKGROUND: There are large knowledge gaps regarding how transmission of 2019 novel coronavirus disease (COVID-19) occurred in different settings across the world. This study aims to summarize basic reproduction number (R0) data and provide clues for designing prevention and control measures. METHODS: Several databases and preprint platforms were retrieved for literature reporting R0 values of COVID-19. The analysis was stratified by the prespecified modeling method to make the R0 values comparable, and by country/region to explore whether R0 estimates differed across the world. The average R0 values were pooled using a random-effects model. RESULTS: We identified 185 unique articles, yielding 43 articles for analysis. The selected studies covered 5 countries from Asia, 5 countries from Europe, 12 countries from Africa, and 1 from North America, South America, and Australia each. Exponential growth rate model was most favored by researchers. The pooled global R0 was 4.08 (95% CI, 3.09-5.39). The R0 estimates for new and shifting epicenters were comparable or even higher than that for the original epicenter Wuhan, China. CONCLUSIONS: The high R0 values suggest that an extraordinary combination of control measures is needed for halting COVID-19.


Subject(s)
Basic Reproduction Number , COVID-19/epidemiology , Global Health , Pneumonia, Viral/epidemiology , Humans , Pandemics , Pneumonia, Viral/virology , SARS-CoV-2
4.
Sci Rep ; 12(1): 19435, 2022 Nov 13.
Article in English | MEDLINE | ID: covidwho-2119152

ABSTRACT

A mathematical model is presented in this paper to investigate the effects of time delay in vaccine production on COVID-19 spread. The model is analyzed qualitatively and numerically. The qualitative analysis indicates that the system variables are non-negative, bounded, and biologically meaningful. Moreover, the model has produced two equilibrium points: the free equilibrium point, which can exist without conditions, and the endemic equilibrium point, which can exist if the control reproduction number, [Formula: see text], is not less than one. In addition, the local stability of the equilibrium points is investigated and agrees with the numerical analysis results. Finally, a sensitivity analysis is conducted for [Formula: see text]. In particular, we examine the effect of the vaccine's time delay, vaccine rate, and vaccine efficiency on the model dynamics.


Subject(s)
COVID-19 , Humans , Basic Reproduction Number , Computer Simulation , COVID-19/prevention & control , Vaccination , Models, Theoretical , Models, Biological
5.
Bull Math Biol ; 84(12): 146, 2022 Nov 11.
Article in English | MEDLINE | ID: covidwho-2117226

ABSTRACT

The statistics of COVID-19 cases exhibits seasonal fluctuations in many countries. In this paper, we propose a COVID-19 epidemic model with seasonality and define the basic reproduction number [Formula: see text] for the disease transmission. It is proved that the disease-free equilibrium is globally asymptotically stable when [Formula: see text], while the disease is uniformly persistent and there exists at least one positive periodic solution when [Formula: see text]. Numerically, we observe that there is a globally asymptotically stable positive periodic solution in the case of [Formula: see text]. Further, we conduct a case study of the COVID-19 transmission in the USA by using statistical data.


Subject(s)
COVID-19 , Humans , Computer Simulation , COVID-19/epidemiology , Models, Biological , Mathematical Concepts , Basic Reproduction Number
6.
Sci Rep ; 12(1): 17888, 2022 Oct 25.
Article in English | MEDLINE | ID: covidwho-2087294

ABSTRACT

During the COVID-19 pandemic, governments faced difficulties in implementing mobility restriction measures, as no clear quantitative relationship between human mobility and infection spread in large cities is known. We developed a model that enables quantitative estimations of the infection risk for individual places and activities by using smartphone GPS data for the Tokyo metropolitan area. The effective reproduction number is directly calculated from the number of infectious social contacts defined by the square of the population density at each location. The difference in the infection rate of daily activities is considered, where the 'stay-out' activity, staying at someplace neither home nor workplace, is more than 28 times larger than other activities. Also, the contribution to the infection strongly depends on location. We imply that the effective reproduction number is sufficiently suppressed if the highest-risk locations or activities are restricted. We also discuss the effects of the Delta variant and vaccination.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Basic Reproduction Number , SARS-CoV-2 , Pandemics
7.
Sci Rep ; 12(1): 18104, 2022 Oct 27.
Article in English | MEDLINE | ID: covidwho-2087286

ABSTRACT

Cross-transmission of information has a profound influence on the progress of science and technology and the discipline integration in the field of education. In this work, knowledge gained from the viral recombination and variation in COVID-19 transmission is applied to information transmission. Virus recombination and virus variation are similar to the crossing and information fusion phenomena in information transmission. An S2I4MR model with information crossing and variation is constructed. Then, the local and global asymptotic stabilities of the information-free equilibrium and information-existence equilibrium are analyzed. Additionally, the basic reproduction number [Formula: see text] of the model is calculated. As such, an optimal control strategy is hereby proposed to promote the cross-transmission of information and generate variant information. The numerical simulations support the results of the theoretical analysis and the sensitivity of the system towards certain control parameters. In particular, the results show that strengthening information crossing promotes the generation of variant information. Furthermore, encouraging information exchange and enhancing education improve the generation of information crossing and information variation.


Subject(s)
COVID-19 , Humans , Basic Reproduction Number , Models, Biological
8.
Sci Rep ; 12(1): 17221, 2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2077104

ABSTRACT

For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for RT-PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of RT-PCR testing capacity. We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of RT-PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). The uncorrected R0 value for the spread of SARS-CoV-2 based on RT-PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95% CI 1.96-2.10) for Covid-19-related deaths. However, because the number of RT-PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.


Subject(s)
Basic Reproduction Number , COVID-19 , COVID-19/epidemiology , COVID-19/mortality , COVID-19 Nucleic Acid Testing , Germany/epidemiology , Humans , Pandemics , Reproducibility of Results , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification
9.
Sci Rep ; 12(1): 17600, 2022 Oct 20.
Article in English | MEDLINE | ID: covidwho-2077096

ABSTRACT

To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number ([Formula: see text]) was calculated based on delayed distribution, Basic Reproductive Number ([Formula: see text]) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on [Formula: see text], and the estimated [Formula: see text] was compared with the actual [Formula: see text]. The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted [Formula: see text] and actual [Formula: see text] was statistically significant. The estimated imported cases and the estimated [Formula: see text] have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure.


Subject(s)
Aviation , COVID-19 , Epidemics , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Basic Reproduction Number , China/epidemiology
10.
Viruses ; 14(10)2022 10 16.
Article in English | MEDLINE | ID: covidwho-2071842

ABSTRACT

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.


Subject(s)
COVID-19 , Humans , SARS-CoV-2/genetics , COVID-19 Testing , Transcriptome , Basic Reproduction Number
11.
PLoS One ; 17(10): e0269464, 2022.
Article in English | MEDLINE | ID: covidwho-2065107

ABSTRACT

In a viral epidemic, the emergence of a novel strain with increased transmissibility (larger value of basic reproduction number R0) sparks the fear that the increase in transmissibility is likely to lead to an increase in disease severity. It is required to investigate if a new, more contagious strain will be necessarily dominant in the population and resulting in more disease severity. In this paper, the impact of the asymptomatic transmission and the emergence time of a more transmissible variant of a multi-strain viral disease on the disease prevalence, disease severity, and the dominant variant in an epidemic was investigated by a proposed 2-strain epidemic model. The simulation results showed that considering only R0, is insufficient to predict the outcome of a new, more contagious strain in the population. A more transmissible strain with a high fraction of asymptomatic cases can substantially reduce the mortality rate. If the emergence time of the new strain is closer to the start of the epidemic, the new, more contagious variant has more chance to win the viral competition and be the dominant strain; otherwise, despite being more contagious, it cannot dominate previous strains. In conclusion, three factors of R0, the fraction of asymptomatic transmission, and the emergence time of the new strain are required to correctly determine the prevalence, disease severity, and the winner of the viral competition.


Subject(s)
Epidemics , Influenza, Human , Virus Diseases , Basic Reproduction Number , Humans , Influenza, Human/epidemiology , Severity of Illness Index , Virus Diseases/epidemiology
12.
Int J Environ Res Public Health ; 19(18)2022 Sep 15.
Article in English | MEDLINE | ID: covidwho-2055222

ABSTRACT

OBJECTIVE: This systematic review estimated the pooled R0 for early COVID-19 outbreaks and identified the impact of study-related factors such as methods, study location and study period on the estimated R0. METHODS: We searched electronic databases for human studies published in English between 1 December 2019 and 30 September 2020 with no restriction on country/region. Two investigators independently performed the data extraction of the studies selected for inclusion during full-text screening. The primary outcome, R0, was analysed by random-effects meta-analysis using the restricted maximum likelihood method. RESULTS: We identified 26,425 studies through our search and included 151 articles in the systematic review, among which 81 were included in the meta-analysis. The estimates of R0 from studies included in the meta-analysis ranged from 0.4 to 12.58. The pooled R0 for COVID-19 was estimated to be 2.66 (95% CI, 2.41-2.94). The results showed heterogeneity among studies and strong evidence of a small-study effect. CONCLUSIONS: The high heterogeneity in studies makes the use of the R0 for basic epidemic planning difficult and presents a huge problem for risk assessment and data synthesis. Consensus on the use of R0 for outbreak assessment is needed, and its use for assessing epidemic risk is not recommended.


Subject(s)
COVID-19 , Epidemics , Basic Reproduction Number , COVID-19/epidemiology , Humans , Reproducibility of Results , SARS-CoV-2
13.
Comput Math Methods Med ; 2022: 8239915, 2022.
Article in English | MEDLINE | ID: covidwho-2053437

ABSTRACT

The COVID-19 outbreak has spread all around the world in less than four months. However, the pattern of the epidemic was different according to the countries. We propose this paper to describe the transmission network and to estimate the serial interval and the reproductive number of the novel coronavirus disease (COVID-19) in Burkina Faso, a Sub-Saharan African country. Data from the COVID-19 response team was analyzed. Information on the 804 first detected cases were pulled together. From contact tracing information, 126 infector-infectee pairs were built. The principal infection clusters with their index cases were observed, principally the two major identified indexes in Burkina. However, the generations of infections were usually short (less than four). The serial interval was estimated to follow a gamma distribution with a shape parameter 1.04 (95% credibility interval: 0.69-1.57) and a scale parameter of 5.69 (95% credibility interval: 3.76-9.11). The basic reproductive number was estimated at 2.36 (95% confidence interval: 1.46-3.26). However, the effective reproductive number decreases very quickly, reaching a minimum value of 0.20 (95% confidence interval: 0.06-0.34). Estimated parameters are made available to monitor the outbreak in Sub-Saharan African countries. These show serial intervals like in the other continents but less infectiousness.


Subject(s)
COVID-19 , Basic Reproduction Number , Burkina Faso/epidemiology , COVID-19/epidemiology , Disease Outbreaks , Humans , SARS-CoV-2
14.
J Biol Dyn ; 16(1): 665-712, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2028933

ABSTRACT

In this paper we assess the effectiveness of different non-pharmaceutical interventions (NPIs) against COVID-19 utilizing a compartmental model. The local asymptotic stability of equilibria (disease-free and endemic) in terms of the basic reproduction number have been determined. We find that the system undergoes a backward bifurcation in the case of imperfect quarantine. The parameters of the model have been estimated from the total confirmed cases of COVID-19 in India. Sensitivity analysis of the basic reproduction number has been performed. The findings also suggest that effectiveness of face masks plays a significant role in reducing the COVID-19 prevalence in India. Optimal control problem with several control strategies has been investigated. We find that the intervention strategies including implementation of lockdown, social distancing, and awareness only, has the highest cost-effectiveness in controlling the infection. This combined strategy also has the least value of average cost-effectiveness ratio (ACER) and associated cost.


Subject(s)
COVID-19 , Basic Reproduction Number , COVID-19/epidemiology , Communicable Disease Control , Cost-Benefit Analysis , Humans , Models, Biological
15.
Comput Math Methods Med ; 2022: 7706447, 2022.
Article in English | MEDLINE | ID: covidwho-2020531

ABSTRACT

In this study, the extended SEIR dynamical model is formulated to investigate the spread of coronavirus disease (COVID-19) via a special focus on contact with asymptomatic and self-isolated infected individuals. Furthermore, a mathematical analysis of the model, including positivity, boundedness, and local and global stability of the disease-free and endemic equilibrium points in terms of the basic reproduction number, is presented. The sensitivity analysis indicates that reducing the disease contact rate and the transmissibility factor related to asymptomatic individuals, along with increasing the quarantine/self-isolation rate and the contact-tracing process, from the view of flattening the curve for novel coronavirus, are crucial to the reduction in disease-related deaths.


Subject(s)
COVID-19 , Basic Reproduction Number , Contact Tracing , Humans
16.
J Math Biol ; 85(2): 17, 2022 08 01.
Article in English | MEDLINE | ID: covidwho-2014119

ABSTRACT

We considered an SIS functional partial differential model cooperated with spatial heterogeneity and lag effect of media impact. The wellposedness including existence and uniqueness of the solution was proved. We defined the basic reproduction number and investigated the threshold dynamics of the model, and discussed the asymptotic behavior and monotonicity of the basic reproduction number associated with the diffusion rate. The local and global Hopf bifurcation at the endemic steady state was investigated theoretically and numerically. There exists numerical cases showing that the larger the number of basic reproduction number, the smaller the final epidemic size. The meaningful conclusion generalizes the previous conclusion of ordinary differential equation.


Subject(s)
Epidemics , Models, Biological , Basic Reproduction Number
17.
Epidemics ; 40: 100624, 2022 09.
Article in English | MEDLINE | ID: covidwho-2004066

ABSTRACT

The effective reproduction number Rt measures an infectious disease's transmissibility as the number of secondary infections in one reproduction time in a population having both susceptible and non-susceptible hosts. Current approaches do not quantify the uncertainty correctly in estimating Rt, as expected by the observed variability in contagion patterns. We elaborate on the Bayesian estimation of Rt by improving on the Poisson sampling model of Cori et al. (2013). By adding an autoregressive latent process, we build a Dynamic Linear Model on the log of observed Rts, resulting in a filtering type Bayesian inference. We use a conjugate analysis, and all calculations are explicit. Results show an improved uncertainty quantification on the estimation of Rt's, with a reliable method that could safely be used by non-experts and within other forecasting systems. We illustrate our approach with recent data from the current COVID19 epidemic in Mexico.


Subject(s)
COVID-19 , Epidemics , Basic Reproduction Number , Bayes Theorem , COVID-19/epidemiology , Humans , Uncertainty
18.
PLoS Comput Biol ; 18(8): e1009980, 2022 08.
Article in English | MEDLINE | ID: covidwho-2002266

ABSTRACT

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes-with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.


Subject(s)
COVID-19 , SARS-CoV-2 , Basic Reproduction Number , COVID-19/epidemiology , Communicable Disease Control/methods , Disease Outbreaks , Humans
19.
Elife ; 112022 08 08.
Article in English | MEDLINE | ID: covidwho-1988433

ABSTRACT

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.


Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining. Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re. Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time. For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place. The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.


Subject(s)
COVID-19 , SARS-CoV-2 , Basic Reproduction Number , COVID-19/epidemiology , Europe/epidemiology , Humans , Pandemics/prevention & control
20.
PLoS One ; 17(6): e0268995, 2022.
Article in English | MEDLINE | ID: covidwho-1987137

ABSTRACT

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-"sub-populations" (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA's Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson's correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.


Subject(s)
COVID-19 , Basic Reproduction Number , COVID-19/epidemiology , Humans , Pandemics , Population Density , South Carolina/epidemiology
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