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3 pages, 196 KiB

Open AccessEditorial

Editorial: Infectious Disease Epidemiology and Transmission Dynamics

by Zhanwei Du, Wei Luo, Rachel Sippy and Lin Wang

Viruses 2023, 15(1), 246; https://doi.org/10.3390/v15010246 - 15 Jan 2023

Viewed by 1426

Abstract

Infectious diseases, such as COVID-19 [...] Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

Research

Jump to: Editorial

18 pages, 6103 KiB

Open AccessArticle

Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19

by Andrew Lawson and Chawarat Rotejanaprasert

Viruses 2023, 15(2), 325; https://doi.org/10.3390/v15020325 - 24 Jan 2023

Cited by 2 | Viewed by 1105

Abstract

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal [...] Read more.

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal inference, it is classically of interest to investigate the counterfactuals. In the context of STIF, it is possible to use nowcasting to assess the possible counterfactual realization of disease in an incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models are discussed, and the importance of the ST component in nowcasting is assessed. Real examples of lockdowns for COVID-19 in two US states during 2020 and 2021 are provided. The degeneracy in prediction over longer time periods is highlighted, and the wide confidence intervals characterize the forecasts. For SC, the early and short lockdown contrasted with the longer NJ intervention. The approach here demonstrated marked differences in spatio-temporal disparities across counties with respect to an adherence to counterfactual predictions. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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14 pages, 551 KiB

Open AccessArticle

The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission

by Chunyang Zhang, Xiao Zhang, Yuan Bai, Eric H. Y. Lau and Sen Pei

Viruses 2022, 14(12), 2795; https://doi.org/10.3390/v14122795 - 15 Dec 2022

Cited by 1 | Viewed by 1704

Abstract

Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite [...] Read more.

Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite for the model to be applied in practice, by assessing whether the unknown model parameters can be uniquely determined from epidemic data. In this study, we applied a scaling method to analyse the structural identifiability of four types of commonly used humidity-driven epidemiological models. Specifically, we investigated whether the key epidemiological parameters (i.e., infectious period, the average duration of immunity, the average latency period, and the maximum and minimum daily basic reproductive number) can be uniquely determined simultaneously when prevalence data is observable. We found that each model is identifiable when the prevalence of infection is observable. The structural identifiability of these models will lay the foundation for testing practical identifiability in the future using synthetic prevalence data when considering observation noise. In practice, epidemiological models should be examined with caution before using them to estimate model parameters from epidemic data. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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16 pages, 2278 KiB

Open AccessArticle

Spatio-Temporal Epidemiology of the Spread of African Swine Fever in Wild Boar and the Role of Environmental Factors in South Korea

by Satoshi Ito, Jaime Bosch, Hyunkyu Jeong, Cecilia Aguilar-Vega, Jonghoon Park, Marta Martínez-Avilés and Jose Manuel Sánchez-Vizcaíno

Viruses 2022, 14(12), 2779; https://doi.org/10.3390/v14122779 - 13 Dec 2022

Cited by 4 | Viewed by 2291

Abstract

Since the first confirmation of African swine fever (ASF) in domestic pig farms in South Korea in September 2019, ASF continues to expand and most notifications have been reported in wild boar populations. In this study, we first performed a spatio-temporal cluster analysis [...] Read more.

Since the first confirmation of African swine fever (ASF) in domestic pig farms in South Korea in September 2019, ASF continues to expand and most notifications have been reported in wild boar populations. In this study, we first performed a spatio-temporal cluster analysis to understand ASF spread in wild boar. Secondly, generalized linear logistic regression (GLLR) model analysis was performed to identify environmental factors contributing to cluster formation. In the meantime, the basic reproduction number (R₀) for each cluster was estimated to understand the growth of the epidemic. The cluster analysis resulted in the detection of 17 spatio-temporal clusters. The GLLR model analysis identified factors influencing cluster formation and indicated the possibility of estimating ASF epidemic areas based on environmental conditions. In a scenario only considering direct transmission among wild boar, R₀ ranged from 1.01 to 1.5 with an average of 1.10, while, in another scenario including indirect transmission via an infected carcass, R₀ ranged from 1.03 to 4.38 with an average of 1.56. We identified factors influencing ASF expansion based on spatio-temporal clusters. The results obtained would be useful for selecting priority areas for ASF control and would greatly assist in identifying efficient vaccination areas in the future. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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16 pages, 2975 KiB

Open AccessArticle

The Effect of Preventive Measures and Vaccination against SARS-CoV-2 on the Infection Risk, Treatment, and Hospitalization: A Cross-Sectional Study of Algeria

by Ahmed Hamimes, Hani Amir Aouissi, Mostefa Ababsa, Mohamed Lounis, Umesh Jayarajah, Christian Napoli and Zaineb A. Kasemy

Viruses 2022, 14(12), 2771; https://doi.org/10.3390/v14122771 - 12 Dec 2022

Cited by 12 | Viewed by 2434

Abstract

Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus continues to afflict many countries around the world. The resurgence of COVID-19 cases and deaths in many countries shows a complacency in adhering to preventive guidelines. Consequently, vaccination continues to be a crucial intervention to [...] Read more.

Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus continues to afflict many countries around the world. The resurgence of COVID-19 cases and deaths in many countries shows a complacency in adhering to preventive guidelines. Consequently, vaccination continues to be a crucial intervention to reduce the effects of this pandemic. This study investigated the impact of preventive measures and COVID-19 vaccination on the infection, medication, and hospitalization. A cross-sectional online survey was conducted between 23 December 2021 and 12 March 2022 in Algeria. To evaluate the effectiveness of strategies aimed at avoiding and minimizing SARS-CoV-2 infection and severity, a questionnaire was created and validated. Descriptive statistics and logistic regression analyses were computed to identify associations between dependent and independent variables. Variables with a p-value of < 0.05 were considered statistically significant. Our results indicated that out of 2294 answers received, only 16% of our sample was vaccinated, and more than 60% did not apply preventive guidelines. As a result, 45% were infected with SARS-CoV-2, 75% took treatment (even preventive), and 9% were hospitalized. The logistic regression showed that the impact of preventive measures on the unvaccinated is statistically not significant (OR: 0.764, 95% CI = 0. 555–1.052; p = 0.09). However, this relationship changes significantly for people who are vaccinated (OR: 0.108, 95% CI = 0.047–0.248; p < 0.0001). Our results also demonstrated that the impact of protective measures on non-vaccinated individuals is statistically significant in reducing the need to receive anti-COVID-19 treatments (OR: 0.447, 95% CI = 0.321–0.623; p < 0.0001). Furthermore, the results showed that the impact of preventive measures on the non-vaccinated population is also statistically significant in reducing the risk of hospitalization (OR: 0.211, 95% CI = 0.081–0.548; p < 0.0001). Moreover, vaccinated individuals who neglect preventive measures must take the COVID-19 medication at a rate of 3.77 times (OR: 3.77) higher than those who follow preventive measures and are vaccinated. In short, our findings demonstrate the importance of combining preventive measures and vaccination in order to fight against the pandemic. Therefore, we advise the Ministry of Health and relevant authorities to put more effort into enhancing public knowledge about the COVID-19 infection and vaccination through education and awareness initiatives. Parallel to implementing vaccination as additional preventive strategy, behavioral change initiatives must be improved to encourage adherence to COVID-19 prevention recommendations. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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21 pages, 692 KiB

Open AccessArticle

HIV/AIDS Mathematical Model of Triangle Transmission

by Cristian Camilo Espitia Morillo and João Frederico da Costa Azevedo Meyer

Viruses 2022, 14(12), 2749; https://doi.org/10.3390/v14122749 - 09 Dec 2022

Cited by 3 | Viewed by 1930

Abstract

In this paper, a mathematical analysis of the HIV/AIDS deterministic model studied in the paper called Mathematical Model of HIV/AIDS Considering Sexual Preferences Under Antiretroviral Therapy, a case study in the previous works preformed by Espitia is performed. The objective is to gain [...] Read more.

In this paper, a mathematical analysis of the HIV/AIDS deterministic model studied in the paper called Mathematical Model of HIV/AIDS Considering Sexual Preferences Under Antiretroviral Therapy, a case study in the previous works preformed by Espitia is performed. The objective is to gain insight into the qualitative dynamics of the model determining the conditions for the persistence or effective control of the disease in the community through the study of basic properties such as positiveness and boundedness; the calculus of the basic reproduction number; stationary points such as disease-free equilibrium (DFE), boundary equilibrium (BE) and endemic equilibrium (EE); and the local stability (LAS) of disease-free equilibrium. The findings allow us to conclude that the best way to reduce contagion and consequently reach a DFE is thought to be the reduction in the rate of homosexual partners, as they are the most affected population by the virus and are therefore the most likely to become infected and spread it. Increasing the departure rate of infected individuals leads to a decrease in untreated infected heterosexual men and untreated infected women. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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7 pages, 382 KiB

Open AccessArticle

The Preventive Role of mRNA Vaccines in Reducing Death among Moderate Omicron-Infected Patients: A Follow-Up Study

by Amy Ming-Fang Yen, Sam Li-Sheng Chen, Chen-Yang Hsu and Tony Hsiu-Hsi Chen

Viruses 2022, 14(12), 2622; https://doi.org/10.3390/v14122622 - 24 Nov 2022

Cited by 2 | Viewed by 1066

Abstract

Very few studies have been conducted to assess the potential preventive role of vaccines, particularly mRNA vaccines, in the improvement of survival among moderate and severe hospitalized patients with COVID-19. After community-acquired outbreaks of the Omicron variant from 18 March until 31 May [...] Read more.

Very few studies have been conducted to assess the potential preventive role of vaccines, particularly mRNA vaccines, in the improvement of survival among moderate and severe hospitalized patients with COVID-19. After community-acquired outbreaks of the Omicron variant from 18 March until 31 May 2022, occurred in Taiwan, this retrospective cohort of 4090 moderate and 1378 severe patients admitted to hospital was classified according to whether they were administered an mRNA-based vaccine, and followed up to ascertain rates of death in both the vaccinated (≥2 doses) and unvaccinated (no or 1 dose) groups. The age-adjusted hazard ratio (aHR) of less than 1 was used to assess the preventive role of mRNA vaccines in reducing deaths among moderate and severe Omicron-infected patients. Survival was statistically significantly better for the ≥2 dose jab group (aHR, 0.75, 95% confidence interval [CI], 0.60 to 0.94) and even higher among those who had received a booster jab (aHR, 0.71; 95% CI, 0.55 to 0.91) compared with the unvaccinated group among moderate patients, but not among severe patients. In conclusion, unveiling the role of mRNA vaccines in preventing moderate but not severe COVID-19 patients from death provides new insights into how mRNA vaccines play a role in the pathway leading to a severe outcome due to Omicron COVID-19. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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13 pages, 3038 KiB

Open AccessArticle

Estimation of R₀ for the Spread of the First ASF Epidemic in Italy from Fresh Carcasses

by Federica Loi, Daria Di Sabatino, Ileana Baldi, Sandro Rolesu, Vincenzo Gervasi, Vittorio Guberti and Stefano Cappai

Viruses 2022, 14(10), 2240; https://doi.org/10.3390/v14102240 - 12 Oct 2022

Cited by 7 | Viewed by 1843

Abstract

After fifty years of spread in the European continent, the African swine fever (ASF) virus was detected for the first time in the north of Italy (Piedmont) in a wild boar carcass in December, 2021. During the first six months of the epidemic, [...] Read more.

After fifty years of spread in the European continent, the African swine fever (ASF) virus was detected for the first time in the north of Italy (Piedmont) in a wild boar carcass in December, 2021. During the first six months of the epidemic, the central role of wild boars in disease transmission was confirmed by more than 200 outbreaks, which occurred in two different areas declared as infected. The virus entered a domestic pig farm in the second temporal cluster identified in the center of the country (Lazio). Understanding ASF dynamics in wild boars is a prerequisite for preventing the spread, and for designing and applying effective surveillance and control plans. The aim of this work was to describe and evaluate the data collected during the first six months of the ASF epidemic in Italy, and to estimate the basic reproduction number (R₀) in order to quantify the extent of disease spread. The R₀ estimates were significantly different for the two spatio-temporal clusters of ASF in Italy, and they identified the two infected areas based on the time necessary for the number of cases to double (td) and on an exponential decay model. These results (R₀ = 1.41 in Piedmont and 1.66 in Lazio) provide quantitative knowledge on the epidemiology of ASF in Italy. These parameters could represent a fundamental tool for modeling country-specific ASF transmission and for monitoring both the spread and sampling effort needed to detect the disease early. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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24 pages, 14706 KiB

Open AccessArticle

Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks

by Zeyi Liu, Yang Ma, Qing Cheng and Zhong Liu

Viruses 2022, 14(8), 1659; https://doi.org/10.3390/v14081659 - 28 Jul 2022

Cited by 2 | Viewed by 1460

Abstract

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying [...] Read more.

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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17 pages, 3397 KiB

Open AccessArticle

EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number

by Shihui Jin, Borame Lee Dickens, Jue Tao Lim and Alex R. Cook

Viruses 2022, 14(7), 1576; https://doi.org/10.3390/v14071576 - 20 Jul 2022

Cited by 4 | Viewed by 1742

Abstract

The time-varying reproduction (

R_{t}

) provides a real-time estimate of pathogen transmissibility and may be influenced by exogenous factors such as mobility and mitigation measures which are not directly related to epidemiology parameters and observations. Meanwhile, evaluating the impacts of these [...] Read more.

The time-varying reproduction (

R_{t}

) provides a real-time estimate of pathogen transmissibility and may be influenced by exogenous factors such as mobility and mitigation measures which are not directly related to epidemiology parameters and observations. Meanwhile, evaluating the impacts of these factors is vital for policy makers to propose and adjust containment strategies. Here, we developed a Bayesian regression framework, EpiRegress, to provide

R_{t}

estimates and assess impacts of diverse factors on virus transmission, utilising daily case counts, mobility, and policy data. To demonstrate the method’s utility, we used simulations as well as data in four regions from the Western Pacific with periods of low COVID-19 incidence, namely: New South Wales, Australia; New Zealand; Singapore; and Taiwan, China. We found that imported cases had a limited contribution on the overall epidemic dynamics but may degrade the quality of the

R_{t}

estimate if not explicitly accounted for. We additionally demonstrated EpiRegress’s capability in nowcasting disease transmissibility before contemporaneous cases diagnosis. The approach was proved flexible enough to respond to periods of atypical local transmission during epidemic lulls and to periods of mass community transmission. Furthermore, in epidemics where travel restrictions are present, it is able to distinguish the influence of imported cases. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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20 pages, 3226 KiB

Open AccessArticle

Modeling the Impact of Vaccination on COVID-19 and Its Delta and Omicron Variants

by Jianbo Wang, Yin-Chi Chan, Ruiwu Niu, Eric W. M. Wong and Michaël Antonie van Wyk

Viruses 2022, 14(7), 1482; https://doi.org/10.3390/v14071482 - 06 Jul 2022

Cited by 13 | Viewed by 1989

Abstract

Vaccination is an important means to fight against the spread of the SARS-CoV-2 virus and its variants. In this work, we propose a general susceptible-vaccinated-exposed-infected-hospitalized-removed (SVEIHR) model and derive its basic and effective reproduction numbers. We set Hong Kong as an example and [...] Read more.

Vaccination is an important means to fight against the spread of the SARS-CoV-2 virus and its variants. In this work, we propose a general susceptible-vaccinated-exposed-infected-hospitalized-removed (SVEIHR) model and derive its basic and effective reproduction numbers. We set Hong Kong as an example and calculate conditions of herd immunity for multiple vaccines and disease variants. The model shows how the number of confirmed COVID-19 cases in Hong Kong during the second and third waves of the COVID-19 pandemic would have been reduced if vaccination were available then. We then investigate the relationships between various model parameters and the cumulative number of hospitalized COVID-19 cases in Hong Kong for the ancestral, Delta, and Omicron strains. Numerical results demonstrate that the static herd immunity threshold corresponds to one percent of the population requiring hospitalization or isolation at some point in time. We also demonstrate that when the vaccination rate is high, the initial proportion of vaccinated individuals can be lowered while still maintaining the same proportion of cumulative hospitalized/isolated individuals. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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17 pages, 2993 KiB

Open AccessArticle

Strategies to Mitigate Establishment under the Wolbachia Incompatible Insect Technique

by Stacy Soh, Soon Hoe Ho, Janet Ong, Annabel Seah, Borame Sue Dickens, Ken Wei Tan, Joel Ruihan Koo, Alex R. Cook, Shuzhen Sim, Cheong Huat Tan, Lee Ching Ng and Jue Tao Lim

Viruses 2022, 14(6), 1132; https://doi.org/10.3390/v14061132 - 24 May 2022

Cited by 7 | Viewed by 2447

Abstract

The Incompatible Insect Technique (IIT) strategy involves the release of male mosquitoes infected with the bacterium Wolbachia. Regular releases of male Wolbachia-infected mosquitoes can lead to the suppression of mosquito populations, thereby reducing the risk of transmission of vector-borne diseases such [...] Read more.

The Incompatible Insect Technique (IIT) strategy involves the release of male mosquitoes infected with the bacterium Wolbachia. Regular releases of male Wolbachia-infected mosquitoes can lead to the suppression of mosquito populations, thereby reducing the risk of transmission of vector-borne diseases such as dengue. However, due to imperfect sex-sorting under IIT, fertile Wolbachia-infected female mosquitoes may potentially be unintentionally released into the environment, which may result in replacement and failure to suppress the mosquito populations. As such, mitigating Wolbachia establishment requires a combination of IIT with other strategies. We introduced a simple compartmental model to simulate ex-ante mosquito population dynamics subjected to a Wolbachia-IIT programme. In silico, we explored the risk of replacement, and strategies that could mitigate the establishment of the released Wolbachia strain in the mosquito population. Our results suggest that mitigation may be achieved through the application of a sterile insect technique. Our simulations indicate that these interventions do not override the intended wild type suppression of the IIT approach. These findings will inform policy makers of possible ways to mitigate the potential establishment of Wolbachia using the IIT population control strategy. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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10 pages, 11373 KiB

Open AccessArticle

Modelling the Impact of Mass Testing to Transition from Pandemic Mitigation to Endemic COVID-19

by Joel R Koo, Alex R Cook, Jue Tao Lim, Ken Wei Tan and Borame L Dickens

Viruses 2022, 14(5), 967; https://doi.org/10.3390/v14050967 - 05 May 2022

Cited by 11 | Viewed by 1724

Abstract

As countries transition from pandemic mitigation to endemic COVID-19, mass testing may blunt the impact on the healthcare system of the liminal wave. We used GeoDEMOS-R, an agent-based model of Singapore’s population with demographic distributions and vaccination status. A 250-day COVID-19 Delta variant [...] Read more.

As countries transition from pandemic mitigation to endemic COVID-19, mass testing may blunt the impact on the healthcare system of the liminal wave. We used GeoDEMOS-R, an agent-based model of Singapore’s population with demographic distributions and vaccination status. A 250-day COVID-19 Delta variant model was run at varying maximal rapid antigen test sensitivities and frequencies. Without testing, the number of infections reached 1,021,000 (899,400–1,147,000) at 250 days. When conducting fortnightly and weekly mass routine rapid antigen testing 30 days into the outbreak at a maximal test sensitivity of 0.6, this was reduced by 12.8% (11.3–14.5%) and 25.2% (22.5–28.5%). An increase in maximal test sensitivity of 0.2 results a corresponding reduction of 17.5% (15.5–20.2%) and 34.4% (30.5–39.1%). Within the maximal test sensitivity range of 0.6–0.8, test frequency has a greater impact than maximal test sensitivity with an average reduction of 2.2% in infections for each day removed between tests in comparison to a 0.43% average reduction per 1% increase in test frequency. Our findings highlight that mass testing using rapid diagnostic tests can be used as an effective intervention for countries transitioning from pandemic mitigation to endemic COVID-19. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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5 pages, 783 KiB

Open AccessCommunication

Reproduction Number of the Omicron Variant Triples That of the Delta Variant

by Zhanwei Du, Huaping Hong, Shuqi Wang, Lijia Ma, Caifen Liu, Yuan Bai, Dillon C. Adam, Linwei Tian, Lin Wang, Eric H. Y. Lau and Benjamin J. Cowling

Viruses 2022, 14(4), 821; https://doi.org/10.3390/v14040821 - 15 Apr 2022

Cited by 33 | Viewed by 3914

Abstract

COVID-19 remains a persistent threat, especially with the predominant Omicron variant emerging in early 2022, presenting with high transmissibility, immune escape, and waning. There is a need to rapidly ramp up global vaccine coverage while enhancing public health and social measures. Timely and [...] Read more.

COVID-19 remains a persistent threat, especially with the predominant Omicron variant emerging in early 2022, presenting with high transmissibility, immune escape, and waning. There is a need to rapidly ramp up global vaccine coverage while enhancing public health and social measures. Timely and reliable estimation of the reproduction number throughout a pandemic is critical for assessing the impact of mitigation efforts and the potential need to adjust for control measures. We conducted a systematic review on the reproduction numbers of the Omicron variant and gave the pooled estimates. We identified six studies by searching PubMed, Embase, Web of Science, and Google Scholar for articles published between 1 January 2020 and 6 March 2022. We estimate that the effective reproduction number ranges from 2.43 to 5.11, with a pooled estimate of 4.20 (95% CI: 2.05, 6.35). The Omicron variant has an effective reproduction number which is triple (2.71 (95% CI: 1.86, 3.56)) that of the Delta variant. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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6 pages, 740 KiB

Open AccessCommunication

Antiviral Efficacy of Molnupiravir for COVID-19 Treatment

by Yuan Bai, Mingwang Shen and Lei Zhang

Viruses 2022, 14(4), 763; https://doi.org/10.3390/v14040763 - 06 Apr 2022

Cited by 9 | Viewed by 2571

Abstract

The ongoing global pandemic of COVID-19 poses unprecedented public health risks for governments and societies around the world, which have been exacerbated by the emergence of SARS-CoV-2 variants. Pharmaceutical interventions with high antiviral efficacy are expected to delay and contain the COVID-19 pandemic. [...] Read more.

The ongoing global pandemic of COVID-19 poses unprecedented public health risks for governments and societies around the world, which have been exacerbated by the emergence of SARS-CoV-2 variants. Pharmaceutical interventions with high antiviral efficacy are expected to delay and contain the COVID-19 pandemic. Molnupiravir, as an oral antiviral prodrug, is active against SARS-CoV-2 and is now (23 February 2022) one of the seven widely-used coronavirus treatments. To estimate its antiviral efficacy of Molnupiravir, we built a granular mathematical within-host model. We find that the antiviral efficacy of Molnupiravir to stop the growth of the virus is 0.56 (95% CI: 0.49, 0.64), which could inhibit 56% of the replication of infected cells per day. There has been good progress in developing high-efficacy antiviral drugs that rapidly reduce viral load and may also reduce the infectiousness of treated cases if administered as early as possible. Full article

(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

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