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Wastewater surveillance (WWS) can leverage its wide coverage, population-based sampling, and high monitoring frequency to capture citywide pandemic trends independent of clinical surveillance. Here we conducted a nine months daily WWS for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 12 wastewater treatment plants (WWTPs), covering approximately 80% of the population, to monitor infection dynamics in Hong Kong, China. We found that the SARS-CoV-2 virus concentration in wastewater was correlated with the daily number of reported cases and reached two pandemic peaks three days earlier during the study period. In addition, two different methods were established to estimate the prevalence/incidence rates from wastewater measurements. The estimated results from wastewater were consistent with findings from two independent citywide clinical surveillance programmes (rapid antigen test (RAT) surveillance and serology surveillance), but higher than the cases number reported by the Centre for Health Protection (CHP) of Hong Kong, China. Moreover, the effective reproductive number (Rt) was estimated from wastewater measurements to reflect both citywide and regional transmission dynamics. Our findings demonstrate that large-scale intensive WWS from WWTPs provides cost-effective and timely public health information, especially when the clinical surveillance is inadequate and costly. This approach also provides insights into pandemic dynamics at higher spatiotemporal resolutions, facilitating the formulation of effective control policies and targeted resource allocation.

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

The pandemic caused by the novel coronavirus COVID‐19 has impact the economies of countries across the world. In a short period of time, researchers have begun to analyse the effect of the pandemic on global stock markets. Although the most known measurements of COVID‐19 are the number of new cases and deaths, there are more robust indicators. In particular, the effective reproductive number is one of the most important indicators to analyse the pandemic which indicates the degree to which the spread is under control. In this paper, we assess the impact that the Effective Reproductive Number (Rt) has on 26 countries around the world (32 stock market indexes) comparing the performance of various forms of Generalized AutoRegressive Conditional Heteroskedasticity models. The results demonstrate that of the 32 stock markets analysed, 37.5% had a negative effect with respect to Rt and only in 12.5% of the cases was the effect of the variation of Rt positive. This implies that in more than a third of the stock markets analysed as the pandemic progressed uncontrolled the result was a decrease in the value of the market index. The 11 of the 26 countries analysed had a negative and significant effect (Brazil, Germany, Indonesia, Israel, Italy, Japan, Russia, South Korea, Sweden, Taiwan, and United States). Findings suggest that the Effective Reproductive Number volatility had a significant impact on 10 of the 26 countries analysed (38.5%) (Australia, Brazil, Canada, China, India, Italy, Mexico, Russia, Singapore and United Kingdom). [ABSTRACT FROM AUTHOR]

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (Rt) estimated against time, a more realistic than the static R0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations. [ABSTRACT FROM AUTHOR]

Since the beginning of the pandemic, SARS‐CoV‐2 has shown a great genomic variability, resulting in the continuous emergence of new variants that has made their global monitoring and study a priority. This work aimed to study the genomic heterogeneity, the temporal origin, the rate of viral evolution and the population dynamics of the main circulating variants (20E.EU1, Alpha and Delta) in Italy, in August 2020–January 2022 period. For phylogenetic analyses, three datasets were set up, each for a different main lineage/variant circulating in Italy in that time including other Italian and International sequences of the same lineage/variant, available in GISAID sampled in the same times. The international dataset showed 26 (23% Italians, 23% singleton, 54% mixed), 40 (60% mixed, 37.5% Italians, 1 singleton) and 42 (85.7% mixed, 9.5% singleton, 4.8% Italians) clusters with at least one Italian sequence, in 20E.EU1 clade, Alpha and Delta variants, respectively. The estimation of tMRCAs in the Italian clusters (including >70% of genomes from Italy) showed that in all the lineage/variant, the earliest clusters were the largest in size and the most persistent in time and frequently mixed. Isolates from the major Italian Islands tended to segregate in clusters more frequently than those from other part of Italy. The study of infection dynamics showed a positive correlation between the trend in the effective number of infections estimated by BSP model and the Re curves estimated by birth‐death skyline plot. The present work highlighted different evolutionary dynamics of studied lineages with high concordance between epidemiological parameters estimation and phylodynamic trends suggesting that the mechanism of replacement of the SARS‐CoV‐2 variants must be related to a complex of factors involving the transmissibility, as well as the implementation of control measures, and the level of cross‐immunization within the population. [ABSTRACT FROM AUTHOR]

Abstract Background Accurate estimation of the effective reproductive number ( $$R_e$$ R e ) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the $$R_e$$ R e estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. Results The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. Conclusions The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of $$R_e$$ R e .

Background: Accurate estimation of the effective reproductive number ( R e ) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the R e estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. Results: The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. Conclusions: The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of R e . [ABSTRACT FROM AUTHOR]

Classical compartmental models of infectious disease assume that spread occurs through a homogeneous population. This produces poor fits to real data, because individuals vary in their number of epidemiologically-relevant contacts, and hence in their ability to transmit disease. In particular, network theory suggests that super-spreading events tend to happen more often at the beginning of an epidemic, which is inconsistent with the homogeneity assumption. In this paper we argue that a flexible decay shape for the effective reproductive number (Rt) indexed by the susceptible fraction (St) is a theory-informed modeling choice, which better captures the progression of disease incidence over human populations. This, in turn, produces better retrospective fits, as well as more accurate prospective predictions of observed epidemic curves. We extend this framework to fit multi-wave epidemics, and to accommodate public health restrictions on mobility. We demonstrate the performance of this model by doing a prediction study over two years of the SARS-CoV2 pandemic.

ABSTRACT Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Background: Malaysia currently faces significant challenges in controlling the spread of dog-mediated human rabies, as evidenced by recurrent outbreaks in newly affected areas and increasing human fatalities., Materials and Methods: A cross-sectional study was conducted to analyse surveillance data from 2015 to 2023 to examine the epidemiological characteristics of rabies in Malaysia. Data from multiple sources were used, and descriptive statistics, incidence rates, and reproductive numbers were calculated. QGIS software was used to map the distribution of rabies cases, and statistical methods were employed to evaluate associations between rabies incidence, vaccination coverage, and risk factors. We further explored the effectiveness of vaccination campaigns and public health interventions in reducing rabies transmission., Results: Our findings revealed 995 confirmed rabies cases in animals. Sarawak reported the highest proportion of rabies cases at 97.99 %, showing a significant correlation between location and rabies cases (p < .001). Dogs comprised 89.35 % of confirmed cases, while the average annual vaccination rate was only 16.66 %, far below the 59.05 % needed for herd immunity. Moreover, human rabies cases reported in Sarawak had an alarmingly high fatality rate of 90.28 %, highlighting the urgent need for improved public health measures and surveillance., Conclusion: This research provides critical insights for policymakers and health officials to improve rabies control strategies in Malaysia., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Ltd.)

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Background: Ethiopia is one of the sub-Saharan countries hit hard by the HIV epidemic. Previous studies have shown that subtype C dominates the Ethiopian HIV-1 epidemic, but the evolutionary and temporal dynamics of HIV-1 in Ethiopia have not been closely scrutinized. Understanding the evolutionary and epidemiological pattern of HIV is vital to monitor the spread, evaluate and implement HIV prevention strategies. Methods: We analyzed 1,276 Ethiopian HIV-1 subtype C polymerase (pol sequences), including 144 newly generated sequences, collected from different parts of the country from 1986 to 2017. We employed state-of-art maximum likelihood and Bayesian phylodynamic analyses to comprehensively describe the evolutionary dynamics of the HIV-1 epidemic in Ethiopia. We used Bayesian phylodynamic models to estimate the dynamics of the effective population size (Ne) and reproductive numbers (Re) through time for the HIV epidemic in Ethiopia. Results: Our analysis revealed that the Ethiopian HIV-1 epidemic originated from two independent introductions at the beginning of the 1970s and 1980s from eastern and southern African countries, respectively, followed by epidemic growth reaching its maximum in the early 1990s. We identified three large clusters with a majority of Ethiopian sequences. Phylodynamic analyses revealed that all three clusters were characterized by high transmission rates during the early epidemic, followed by a decline in HIV-1 transmissions after 1990. Re was high (4–6) during the earlier time of the epidemic but dropped significantly and remained low (Re < 1) after the mid-1990. Similarly, with an expected shift in time, the effective population size (Ne) steadily increased until the beginning of 2000, followed by a decline and stabilization until recent years. The phylodynamic analyses corroborated the modeled UNAIDS incidence and prevalence estimates. Conclusion: The rapid decline in the HIV epidemic took place a decade before introducing antiretroviral therapy in Ethiopia and coincided with early behavioral, preventive, and awareness interventions implemented in the country. Our findings highlight the importance of behavioral interventions and antiretroviral therapy scale-up to halt and maintain HIV transmissions at low levels (Re < 1). The phylodynamic analyses provide epidemiological insights not directly available using standard surveillance and may inform the adjustment of public health strategies in HIV prevention in Ethiopia. [ABSTRACT FROM AUTHOR]

Objectives: Since the first case of severe acute respiratory syndrome coronavirus-2, identified in December 2019, in Wuhan in China, the number of cases rapidly increased into a pandemic. Governments worldwide have adopted different strategies and measures to interrupt the transmission of coronavirus disease 2019 (COVID-19). The main objective was to report and evaluate the effectiveness of the adopted measures in North Africa countries. Methods: In these countries, the effective reproductive number R(t), the naïve case fatality rate, and the adjusted case fatality rate were estimated and compared on different dates. Results: The obtained results show that the early strict application of containment measures and confinement could help contain the spread of the epidemic and maintain the number of deaths low. Conclusions: These measures might be useful for other countries that are experiencing the start of local COVID-19 outbreaks. They could also serve to halt the spread of new epidemics or pandemics. [ABSTRACT FROM AUTHOR]

The raging COVID-19 pandemic is arguably the most important threat to global health presently. Although there is currently a vaccine, preventive measures have been proposed to reduce the spread of infection but the efficacy of these interventions, and their likely impact on the number of COVID-19 infections is unknown. In this study, we proposed the SEIQHRS model (susceptible-exposed-infectious-quarantine-hospitalized-recovered-susceptible) model that predicts the trajectory of the epidemic to help plan an effective control strategy for COVID-19 in Ghana. We provided a short-term forecast of the early phase of the epidemic trajectory in Ghana using the generalized growth model. We estimated the effective basic Reproductive number Re in real-time using three different estimation procedures and simulated worse case epidemic scenarios and the impact of integrated individual and government interventions on the epidemic in the long term using compartmental models. The maximum likelihood estimates of Re and the corresponding 95% confidence interval was 2.04 [95% CI: 1.82–2.27; 12th March-7th April 2020]. The Re estimate using the exponential growth method was 2.11 [95% CI: 2.00–2.24] within the same period. The Re estimate using time-dependent (TD) method showed a gradual decline of the Effective Reproductive Number since March 12, 2020 when the first 2 index cases were recorded but the rate of transmission remains high (TD: Re = 2.52; 95% CI: [1.87–3.49]). The current estimate of Re based on the TD method is 1.74 [95% CI: 1.41–2.10; (13th May 2020)] but with comprehensive integrated government and individual level interventions, the Re could reduce to 0.5 which is an indication of the epidemic dying out in the general population. Our results showed that enhanced government and individual-level interventions and the intensity of media coverage could have a substantial effect on suppressing transmission of new COVID-19 cases and reduced death rates in Ghana until such a time that a potent vaccine or drug is discovered.

To investigate the transmission dynamics and temporal and spatial migration characteristics of HIV spread among men who have sex with men (MSM) in China, a total of 1012 HIV-1 partial pol sequences, including five subtypes, were studied. Bayesian analysis were applied for each subtype to infer its dynamic characters including the effective reproductive number (Re) and migration process. The mean curve of each Re was almost always greater than 1 (even the 95% highest posterior density (HPD) lower value) along with time, which supports the necessity for a comprehensive study about risk behaviors among young MSM group in China. We also should reappraise the free treatment strategy, especially the therapeutic effect during the free treatment policy.

BackgroundEthiopia is one of the sub-Saharan countries hit hard by the HIV epidemic. Previous studies have shown that subtype C dominates the Ethiopian HIV-1 epidemic, but the evolutionary and temporal dynamics of HIV-1 in Ethiopia have not been closely scrutinized. Understanding the evolutionary and epidemiological pattern of HIV is vital to monitor the spread, evaluate and implement HIV prevention strategies.MethodsWe analyzed 1,276 Ethiopian HIV-1 subtype C polymerase (pol sequences), including 144 newly generated sequences, collected from different parts of the country from 1986 to 2017. We employed state-of-art maximum likelihood and Bayesian phylodynamic analyses to comprehensively describe the evolutionary dynamics of the HIV-1 epidemic in Ethiopia. We used Bayesian phylodynamic models to estimate the dynamics of the effective population size (Ne) and reproductive numbers (Re) through time for the HIV epidemic in Ethiopia.ResultsOur analysis revealed that the Ethiopian HIV-1 epidemic originated from two independent introductions at the beginning of the 1970s and 1980s from eastern and southern African countries, respectively, followed by epidemic growth reaching its maximum in the early 1990s. We identified three large clusters with a majority of Ethiopian sequences. Phylodynamic analyses revealed that all three clusters were characterized by high transmission rates during the early epidemic, followed by a decline in HIV-1 transmissions after 1990. Re was high (4–6) during the earlier time of the epidemic but dropped significantly and remained low (Re

Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic‐epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model‐based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time‐varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany. [ABSTRACT FROM AUTHOR]

Background Nonpharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) are vital to reducing transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China. Methods In this study, surveillance data of COVID-19 and seasonal influenza in 2018–2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured susceptible/infected/recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios. Results The mean effective reproductive number, R t , of COVID-19 before NPIs was 2.12 (95% confidence interval [CI], 2.02–2.21). By 11 March 2020, the overall reduction in R t of COVID-19 was 66.1% (95% CI, 60.1–71.2%). In the epidemiological year 2019–20, influenza transmissibility was reduced by 34.6% (95% CI, 31.3–38.2%) compared with transmissibility in epidemiological year 2018–19. Under the observed contact pattern changes in China, social distancing had similar efficiency against COVID-19 in 3 different scenarios. By assuming the same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to 48.1% (95% CI, 35.4–58.1%) and 34.6% (95% CI, 31.3–38.2%) reductions of the transmissibility of COVID-19, respectively. Conclusions Though isolation/quarantine is more effective than social distancing, given that the typical basic reproductive number of COVID-19 is 2–3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China. [ABSTRACT FROM AUTHOR]

Modeling infectious disease dynamics has been critical throughout the COVID-19 pandemic. Of particular interest are the incidence, prevalence, and effective reproductive number (Rt). Estimating these quantities is challenging due to under-ascertainment, unreliable reporting, and time lags between infection, onset, and testing. We propose a Multilevel Epidemic Regression Model to Account for Incomplete Data (MERMAID) to jointly estimate Rt, ascertainment rates, incidence, and prevalence over time in one or multiple regions. Specifically, MERMAID allows for a flexible regression model of Rt that can incorporate geographic and time-varying covariates. To account for under-ascertainment, we (a) model the ascertainment probability over time as a function of testing metrics and (b) jointly model data on confirmed infections and population-based serological surveys. To account for delays between infection, onset, and reporting, we model stochastic lag times as missing data, and develop an EM algorithm to estimate the model parameters. We evaluate the performance of MERMAID in simulation studies, and assess its robustness by conducting sensitivity analyses in a range of scenarios of model misspecifications. We apply the proposed method to analyze COVID-19 daily confirmed infection counts, PCR testing data, and serological survey data across the United States. Based on our model, we estimate an overall COVID-19 prevalence of 12.5% (ranging from 2.4% in Maine to 20.2% in New York) and an overall ascertainment rate of 45.5% (ranging from 22.5% in New York to 81.3% in Rhode Island) in the United States from March to December 2020. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Traditional methods of quantifying epidemic spread are based on surveillance data. The most widely used surveillance data are normally incidence data from case reports and hospital records, which are normally susceptible to human error, and sometimes, they even can be seriously error-prone and incomplete when collected during a destructive epidemic. In this manuscript, we introduce a new method to study the spread of infectious disease. We gave an example of how to use this method to predict the virus spreading using the HIV gene sequences data of China. First, we applied Bayesian inference to gene sequences of two main subtypes of the HIV virus to infer the effective reproduction number (GRe(t)) to trace the history of HIV transmission. Second, a dynamic model was established to forecast the spread of HIV medication resistance in the future and also obtain its effective reproduction number (MRe(t)). Through fitting the two effective reproduction numbers obtained from the two separate ways above, some crucial parameters for the dynamic model were obtained. Simply raising the treatment rate has no impact on lowering the infection rate, according to the dynamics model research, but would instead increase the rate of medication resistance. The negative relationship between the prevalence of HIV and the survivorship of infected individuals following treatment may be to blame for this. Reducing the MSM population’s number of sexual partners is a more efficient strategy to reduce transmission per the sensitivity analysis.

Effective reproductive numbers (Rt) were calculated from data on the COVID-19 outbreak in China and linked to dates in 2020 when different interventions were enacted. From a maximum of 3.98 before the lockdown in Wuhan City, the values of Rt declined to below 1 by the second week of February, after the construction of hospitals dedicated to COVID-19 patients. The Rt continued to decline following additional measures in line with the policy of “early detection, early report, early quarantine, and early treatment.” The results provide quantitative evaluations of how intervention measures and their timings succeeded, from which lessons can be learned by other countries dealing with future outbreaks.

COVID-19 is causing a significant burden on medical and healthcare resources globally due to high numbers of hospitalisations and deaths recorded as the pandemic continues. This research aims to assess the effects of climate factors (i.e., daily average temperature and average relative humidity) on effective reproductive number of COVID-19 outbreak in Wuhan, China during the early stage of the outbreak. Our research showed that effective reproductive number of COVID-19 will increase by 7.6% (95% Confidence Interval: 5.4% ~ 9.8%) per 1°C drop in mean temperature at prior moving average of 0–8 days lag in Wuhan, China. Our results indicate temperature was negatively associated with COVID-19 transmissibility during early stages of the outbreak in Wuhan, suggesting temperature is likely to effect COVID-19 transmission. These results suggest increased precautions should be taken in the colder seasons to reduce COVID-19 transmission in the future, based on past success in controlling the pandemic in Wuhan, China.

This study aimed to estimate the basic reproduction number and the time-varying estimate of effective reproductive number of COVID-19 in American countries as they implemented non-pharmacological strategies for the containment of the SARS-CoV-2 virus. Data sources included COVID-19 epidemic data from Johns Hopkins University’ data repository and official websites of countries with a relatively high incidence of COVID-19. The maximum likelihood method was used to estimate the and . The results showed that El Salvador, the Dominican Republic, Panama, and Peru have the lowest, while the USA and Canada have the highest. Other American countries have an around 1.4. Countries could be divided into three groups based on the varied behavior of over time. The first group (Mexico, USA, Colombia and Brazil) started with a high, which decreased post-intervention. In the second group, the intervention was performed at the moment when the, is high and it decreased slowly post-intervention (Canada, Argentina, Chile Peru, Panama and Dominican Republic). In the third group (Bolivia, Peru and Guatemala), the, was erratic and could not be attributable to the intervention. There is a close relationship between and non-pharmacological interventions decreed by governments of countries for the control of the COVID-19 pandemic. There are also immediate changes in the behavior of the indicator, and therefore the progression of the outbreak, when the interventions were implemented closer to the index case for each country.

Background: Since identification of the first case on Feb. 14, Egypt had implemented several control measures. This research aimed at study the time trend of the transmissibility and mortality of COVID 19 in Egypt. Methods: Published data on daily reported cases and deaths since the start of the epidemic till week 19 were used. We estimated the basic reproductive number (R0) during the early phase of the epidemic using the simple exponential growth method (SEG) and time dependent method (TD). Then we estimated time varying effective reproductive number (Re) after implementation of the control measures by applying the TD method. Moreover, the trend in the Case Fatality Rate (CFR) throughout the study period was studied. Results: With SEG method, R0 was found to be 2.26 (2.15–2.38) and 2.58 (2.43–2.72) for infectious period of 8 and 10 days, respectively. While by the TD method, R0 was estimated to be 2.34 (95% CrI: 2.05–2.64) and 3.01 (95% CrI: 2.64–3.40) for mean ± SD of SI equals 5.8 ± 2.6 and 7.5 ± 3.4, respectively. With TD method, Re decreased from the initial value of R0 to reach 1.30 (95% crI: 1.17–1.45) in week 7. After that Re values fluctuated closely around 1. CFR reached its peak (7.7%) on April 12 then it decreased to its lowest value (3.4%) after two months before increasing slightly again to (4.1%) in the last days. Conclusion and recommendation: The initial Basic reproductive number was high in Egypt. Effective reproductive number dropped after control measures till fluctuating around one. CFR also declined over time but slight increase in the last days was observed. After relaxation of the control measures, we recommend the instantaneous monitoring of the transmissibility and mortality in Egypt. [ABSTRACT FROM AUTHOR]

Hefei, Anhui province, is one of the cities in the Yangtze River Delta, where many people migrate to Jiangsu, Zhejiang and Shanghai. High migration also contributes to the HIV epidemic. This study explored the HIV prevalence in Hefei to provide a reference for other provinces and assist in the prevention and control of HIV in China. A total of 816 newly reported people with HIV in Hefei from 2017 to 2020 were recruited as subjects. HIV subtypes were identified by a phylogenetic tree. The most prevalent subtypes were CRF07_BC (41.4%), CRF01_AE (38.1%) and CRF55_01B (6.3%). Molecular networks were inferred using HIV-TRACE. The largest and most active transmission cluster was CRF55_01B in Hefei’s network. A Chinese national database (50,798 sequences) was also subjected to molecular network analysis to study the relationship between patients in Hefei and other provinces. CRF55_01B and CRF07_BC-N had higher clustered and interprovincial transmission rates in the national molecular network. People with HIV in Hefei mainly transmitted the disease within the province. Finally, we displayed the epidemic trend of HIV in Hefei in recent years with the dynamic change of effective reproductive number (Re). The weighted overall Re increased rapidly from 2012 to 2015, with a peak value of 3.20 (95% BCI, 2.18–3.85). After 2015, Re began to decline and remained stable at around 1.80. In addition, the Re of CRF55_01B was calculated to be between 2.0 and 4.0 in 2018 and 2019. More attention needs to be paid to the rapid spread of CRF55_01B and CRF07_BC-N strains among people with HIV and the high Re in Hefei. These data provide necessary support to guide the targeted prevention and control of HIV.

Using the parameterized susceptible‐exposed‐infectious‐recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID‐19) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the data‐fitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS‐CoV‐2) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the four‐phase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients' being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases. Research Highlights: The SEIR model showed the peak existing confirmed cases of 59769 arriving on 15 February 2020, with high precision of the data fitting results. The dynamic changes of R values were taken into account on different scenarios of the epidemic. The declines of R indicated the effectiveness of the four‐phase government interventions. Given the situation of pandemic potential, the stringent measures by Chinese government could be referential and enlightening to the other countries suffering from COVID‐19. [ABSTRACT FROM AUTHOR]

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed. [ABSTRACT FROM AUTHOR]

Highlights • Outbreak potential is greater than expected if non-vaccination is clustered. • Vaccination targets are insufficient to achieve herd immunity in many settings. • Impact of susceptibility clustering highest in countries near disease elimination. • Countries with high vaccination should shift focus to local vaccination targets. Abstract Measles elimination efforts are primarily focused on achieving and maintaining national vaccination coverage goals, based on estimates of the critical vaccination threshold (V c): the proportion of the population that must be immune to prevent sustained epidemics. Traditionally, V c estimates assume evenly mixing populations, an invalid assumption. If susceptible individuals preferentially contact one another, communities may remain vulnerable to epidemics even when vaccination coverage targets are met at the national level. Here we present a simple method to estimate V c and the effective reproductive number, R , while accounting for spatial clustering of susceptibility. For measles, assuming R 0 = 15 and 95% population immunity, adjustment for high clustering of susceptibility increases R from 0.75 to 1.29, V c from 93% to 96%, and outbreak probability after a single introduction from <1% to 23%. The impact of clustering remains minimal until vaccination coverage nears elimination levels. We illustrate our approach using Demographic and Health Survey data from Tanzania and show how non-vaccination clustering potentially contributed to continued endemic transmission of measles virus during the last two decades. Our approach demonstrates why high national vaccination coverage sometimes fails to achieve measles elimination, and that a shift from national to subnational focus is needed as countries approach elimination. [ABSTRACT FROM AUTHOR]

Classical compartmental models of infectious disease assume that spread occurs through a homogeneous population. This produces poor fits to real data, because individuals vary in their number of epidemiologically-relevant contacts, and hence in their ability to transmit disease. In particular, network theory suggests that super-spreading events tend to happen more often at the beginning of an epidemic, which is inconsistent with the homogeneity assumption. In this paper we argue that a flexible decay shape for the effective reproductive number (R t) indexed by the susceptible fraction (S t) is a theory-informed modeling choice, which better captures the progression of disease incidence over human populations. This, in turn, produces better retrospective fits, as well as more accurate prospective predictions of observed epidemic curves. We extend this framework to fit multi-wave epidemics, and to accommodate public health restrictions on mobility. We demonstrate the performance of this model by doing a prediction study over two years of the SARS-CoV2 pandemic. [ABSTRACT FROM AUTHOR]

• Effective reproductive numbers (R t) for COVID-19 are linked to dates of interventions. • From 3.98 before lockdown, R t declined to below 1 by the second week of February 2020. • Declines follow "early detection, early report, early quarantine and early treatment". • Evaluations of interventions provide lessons for others. Effective reproductive numbers (R t) were calculated from data on the COVID-19 outbreak in China and linked to dates in 2020 when different interventions were enacted. From a maximum of 3.98 before the lockdown in Wuhan City, the values of R t declined to below 1 by the second week of February, after the construction of hospitals dedicated to COVID-19 patients. The R t continued to decline following additional measures in line with the policy of "early detection, early report, early quarantine, and early treatment." The results provide quantitative evaluations of how intervention measures and their timings succeeded, from which lessons can be learned by other countries dealing with future outbreaks. [ABSTRACT FROM AUTHOR]

The presence of human immunodeficiency virus type 1 (HIV-1) drug resistance among drug-naïve patients remains stable, although the proportion of patients with virological failure to therapy is decreasing. The dynamics of transmitted resistance among drug-naïve patients remains largely unknown. The prevalence of non-nucleoside reverse transcriptase inhibitors (NNRTI) resistance was 16.9% among treatment-naïve individuals in Greece. We aimed to investigate the transmission dynamics and the effective reproductive number (Re) of the locally transmitted NNRTI resistance. We analyzed sequences with dominant NNRTI resistance mutations (E138A and K103N) found within monophyletic clusters (local transmission networks (LTNs)) from patients in Greece. For the K103N LTN, the Re was >1 between 2008 and the first half of 2013. For all E138A LTNs, the Re was >1 between 1998 and 2015, except the most recent one (E138A&%95;4), where the Re was >1 between 2006 and 2011 and approximately equal to 1 thereafter. K103N and E138A&%95;4 showed similar characteristics with a more recent origin, higher Re during the first years of the sub-epidemics, and a declining trend in the number of transmissions during the last two years. In the remaining LTNs the epidemic was still expanding. Our study highlights the added value of molecular epidemiology to public health. [ABSTRACT FROM AUTHOR]

In this work, we propose a Deterministic Mathematical Model that Combines Infectious but not Blind and Infectious Blind Compartments for Onchocerciasis Transmission and Control. Onchocerciasis is usually the term used to describe river blindness, it is a disease that causes blindness, and the second largest cause of blindness after trachoma. It mainly affects the eyes and the skin. The equilibrium states of the model are obtained. The disease free equilibrium state is analysed for stability; the condition for its stability is obtained as an inequality constraint on the parameters. Results shows that although, a 60% treatment coverage rate of infected and infectious blind individuals only is better than 80% treatment coverage rate of infected but not blind individuals only. Also, all the four control strategies reduce the effective reproduction number below unity. A 40% coverage rate of fumigation and treatment of infectious but not blind is better than a 40%coverage rate of fumigation only. It further reveals that a 30% coverage rate of fumigation and treatment of infectious blind is better than 80%coverage rate of fumigation only or fumigation and treatment of infected but not blind only. We are able to show that disease free equilibrium and endemic equilibrium exists and are both locally and globally stable, and we computed the Rc of the model and showed that it is a parameter to test for stability, we also use the Jacobi stability technique to show that disease free equilibrium and endemic equilibrium are both locally and globally stable. The sensitivity analysis results shows that the most sensitive parameter is ρ while the least sensitive is μv Keywords: Onchocerciasis, Mathematical model, Equilibrium state, Deterministic, Effective reproductive number, Stability,

In this work, we propose a Deterministic Mathematical Model that Combines Infectious but not Blind and Infectious Blind Compartments for Onchocerciasis Transmission and Control. Onchocerciasis is usually the term used to describe river blindness, it is a disease that causes blindness, and the second largest cause of blindness after trachoma. It mainly affects the eyes and the skin. The equilibrium states of the model are obtained. The disease free equilibrium state is analysed for stability; the condition for its stability is obtained as an inequality constraint on the parameters. Results shows that although, a 60% treatment coverage rate of infected and infectious blind individuals only is better than 80% treatment coverage rate of infected but not blind individuals only. Also, all the four control strategies reduce the effective reproduction number below unity. A 40% coverage rate of fumigation and treatment of infectious but not blind is better than a 40%coverage rate of fumigation only. It further reveals that a 30% coverage rate of fumigation and treatment of infectious blind is better than 80%coverage rate of fumigation only or fumigation and treatment of infected but not blind only. We are able to show that disease free equilibrium and endemic equilibrium exists and are both locally and globally stable, and we computed the Rc of the model and showed that it is a parameter to test for stability, we also use the Jacobi stability technique to show that disease free equilibrium and endemic equilibrium are both locally and globally stable. The sensitivity analysis results shows that the most sensitive parameter is ρ while the least sensitive is μv. [ABSTRACT FROM AUTHOR]

Background A significant increase in HIV-1 diagnoses was reported among Injecting Drug Users (IDUs) in the Athens (17-fold) and Bucharest (9-fold) metropolitan areas starting 2011. Methods Molecular analyses were conducted on HIV-1 sequences from IDUs comprising 51% and 20% of the diagnosed cases among IDUs during 2011–2013 for Greece and Romania, respectively. Phylodynamic analyses were performed using the newly developed birth–death serial skyline model which allows estimating of important epidemiological parameters, as implemented in BEAST programme. Results Most infections (>90%) occurred within four and three IDU local transmission networks in Athens and Bucharest, respectively. For all Romanian clusters, the viral strains originated from local circulating strains, whereas in Athens, the local strains seeded only two of the four sub-outbreaks. Birth–death skyline plots suggest a more explosive nature for sub-outbreaks in Bucharest than in Athens. In Athens, two sub-outbreaks had been controlled ( Re < 1.0) by 2013 and two appeared to be endemic ( Re ∼ 1). In Bucharest one outbreak continued to expand ( Re > 1.0) and two had been controlled ( Re < 1.0). The lead times were shorter for the outbreak in Athens than in Bucharest. Conclusions Enhanced molecular surveillance proved useful to gain information about the origin, causal pathways, dispersal patterns and transmission dynamics of the outbreaks that can be useful in a public health setting. [ABSTRACT FROM AUTHOR]

This paper concentrates on the tuberculosis data of China from January 2005 to December 2012. We set up a mathematical model to fit those data with the goodness of fit and obtain the optimal parameter values of the model. By the Chi-square test of the statistical inference, the optimal parameter values of the model are reasonable. We get the effective reproductive number of the disease for each year, and also investigate the preventive measures to control the tuberculosis. [ABSTRACT FROM AUTHOR]

Background The 1918 influenza pandemic caused disproportionately high mortality among certain age groups. The mechanisms underlying these differences are not fully understood. Objectives To explore the dynamics of the 1918 pandemic and to identify potential age-specific transmission patterns. Methods We examined 1915-1923 daily mortality data in New York City ( NYC) and estimated the outbreak duration and initial effective reproductive number ( Re) for each 1-year age cohort. Results Four pandemic waves occurred from February 1918 to April 1920. The fractional mortality increase (i.e. ratio of excess mortality to baseline mortality) was highest among teenagers during the first wave. This peak shifted to 25- to 29-year-olds in subsequent waves. The distribution of age-specific mortality during the last three waves was strongly correlated ( r = 0·94 and 0·86). With each wave, the pandemic appeared to spread with a comparable early growth rate but then attenuate with varying rates. For the entire population, Re estimates made assuming 2-day serial interval were 1·74 (1·27), 1·74 (1·43), 1·66 (1·25), and 1·86 (1·37), respectively, during the first week (first 3 weeks) of each wave. Using age-specific mortality, the average Re estimates over the first week of each wave were 1·62 (95% CI: 1·55-1·68), 1·68 (1·65-1·72), 1·67 (1·61-1·73), and 1·69 (1·63-1·74), respectively; Re was not significantly different either among age cohorts or between waves. Conclusions The pandemic generally caused higher mortality among young adults and might have spread mainly among school-aged children during the first wave. We propose mechanisms to explain the timing and transmission dynamics of the four NYC pandemic waves. [ABSTRACT FROM AUTHOR]