Estimates Of R0 Research Articles

The Reproduction Number of Swine Viral Respiratory Diseases: A Systematic Review.

Diseases in the swine industry can cause significant economic and health impacts. This review examines R0 estimates for respiratory diseases in pigs, assessing variations and comparing transmission risks within and between farms. A literature search of three databases aggregated peer-reviewed research articles on swine viral respiratory diseases' R0 values. The study focused on seven diseases: Aujeszky's disease (AD), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Circovirus, Influenza A (IA), Encephalomyocarditis Virus (EV), Classical Swine Fever (CSF), and African Swine Fever (ASF). R0 values were estimated for transmission within and between herds/farms using various methods, from complex mathematical models to simple calculations. Data primarily came from disease surveillance and laboratory experiments. The median R0 for within-herd and between-herd transmission was 10 and 3.31 for AD, 2.78 and 1.14 for PRRSV, 5.9 and 0.89 for Circovirus, 1.75 and 1.6 for CSF, and 3.94 and 3.15 for ASF. For IA and EV, only within-herd R0 values were estimated at 8.65 and 1.3, respectively. Diseases with high R0 values highlight the need for prompt detection and response to outbreaks. Continuous monitoring and evaluation of pathogen transmissibility are crucial for enhancing disease surveillance and reducing the impact of livestock diseases.

Comparing the Change in R0 for the COVID-19 Pandemic in Eight Countries Using an SIR Model for Specific Periods

The reproduction number, R0, is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered.

Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region

BackgroundMore than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005.MethodsBased on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model.ResultsDuring the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI (ADID) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADID and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1.ConclusionsThe present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system.Graphical

COVID‐19 Infection Risk Assessment in a Kindergarten Utilizing Continuous Air Quality Monitoring Data

Researchers and transnational public health organizations have recognized aerosol transmission as an essential route of COVID‐19 transmission. Therefore, improving ventilation systems is now adopted as a core preventive measure. As young children aged 2–6 in kindergartens generally lack vaccine protection and multiple infection clusters have been identified during the pandemic, we aimed to quantify the risk of aerosol transmission in kindergartens in Taipei, Taiwan. From August to November 2021, we conducted on‐site visits and continuously monitored indoor air quality indicators including carbon dioxide (CO2) in a kindergarten located in northern Taiwan. We utilized the Wells–Riley model to estimate the basic reproduction number (R0) of each classroom and staff office, with input parameters including the number of occupants, duration of their stay, and indoor/outdoor CO2 concentration. Contagious settings were defined as those where the R0 estimate exceeded 1. We conducted a scenario/sensitivity analysis to assess the effect of simulated improvement measures. During school hours, the average concentration of CO2 in each classroom and the staff office was often more than 400 ppm higher than the outdoor levels. The R0 estimates gradually increased from Monday to Friday and throughout school hours, corresponding to the hourly and daily distribution of the CO2 concentration, which could not dissipate completely during off‐duty time. The R0 estimates during school hours ranged from 3.01 to 3.12 in classrooms with a maximum of 30 occupants. To lower the R0 estimate, it is imperative to substantially reduce the number of occupants, the duration of their stay, and indoor CO2 concentration. The risk of outbreaks of cluster infections in kindergartens should not be underestimated. Feasible strategies to mitigate this risk should include improving ventilation systems through engineering control and limiting the number of indoor occupants and their time staying indoor through administrative control.

Estimating the potential risk of transmission of arboviruses in the Americas and Europe: a modelling study

Estimates of the spatiotemporal distribution of different mosquito vector species and the associated risk of transmission of arboviruses are key to design adequate policies for preventing local outbreaks and reducing the number of human infections in endemic areas. In this study, we quantified the abundance of Aedes albopictus and Aedes aegypti and the local transmission potential for three arboviral infections at an unprecedented spatiotemporal resolution in areas where no entomological surveillance is available. We developed a computational model to quantify the daily abundance of Aedes mosquitoes, leveraging temperature and precipitation records. The model was calibrated on mosquito surveillance data collected in 115 locations in Europe and the Americas between 2007 and 2018. Model estimates were used to quantify the reproduction number of dengue virus, Zika virus, and chikungunya in Europe and the Americas, at a high spatial resolution. In areas colonised by both Aedes species, A aegypti was estimated to be the main vector for the transmission of dengue virus, Zika virus, and chikungunya, being associated with a higher estimate of R0 when compared with A albopictus. Our estimates highlighted that these arboviruses were endemic in tropical and subtropical countries, with the highest risks of transmission found in central America, Venezuela, Colombia, and central-east Brazil. A non-negligible potential risk of transmission was also estimated for Florida, Texas, and Arizona (USA). The broader ecological niche of A albopictus could contribute to the emergence of chikungunya outbreaks and clusters of dengue autochthonous cases in temperate areas of the Americas, as well as in mediterranean Europe (in particular, in Italy, southern France, and Spain). Our results provide a comprehensive overview of the transmission potential of arboviral diseases in Europe and the Americas, highlighting areas where surveillance and mosquito control capacities should be prioritised. EU and Ministero dell'Università e della Ricerca, Italy (Piano Nazionale di Ripresa e Resilienza Extended Partnership initiative on Emerging Infectious Diseases); EU (Horizon 2020); Ministero dell'Università e della Ricerca, Italy (Progetti di ricerca di Rilevante Interesse Nazionale programme); Brazilian National Council of Science, Technology and Innovation; Ministry of Health, Brazil; and Foundation of Research for Minas Gerais, Brazil.

Epidemiological Assessment of African Swine Fever Spread in the Dominican Republic.

Since African Swine Fever (ASF) was detected in the Dominican Republic in July 2021, it has negatively impacted the country's swine industry. Assessing the epidemiological situation is crucial to helping local authorities and industry stakeholders control the disease. Here, data on 155 reported outbreaks in the Dominican Republic from November 2022 to June 2023 were evaluated. Descriptive spatiotemporal analysis was performed to characterize disease distribution and spread, and between-herd R0 was calculated for the study period. The Knox test and a space-time permutation model were used to evaluate clustering. Data on clinical presentation, biosecurity measures, and suspected reasons for introduction were categorized and summarized. The majority (78%) of outbreaks occurred on backyard farms which generally had low biosecurity. Across farm types, the majority of pigs were still alive at the time of depopulation. Spatiotemporal findings and R0 estimates suggest an endemic pattern of disease geographically located centrally within the country. Clustering was detected even at small temporal and spatial distances due to outbreaks amongst neighboring backyard farms. These results provide critical information on the current state of the ASF epidemic in the Dominican Republic and will aid government officials and swine industry leaders in developing effective ASF control strategies.

Determining Herd Immunity Thresholds for Hepatitis A Virus Transmission to Inform Vaccination Strategies Among People Who Inject Drugs in 16 US States.

Widespread outbreaks of person-to-person transmitted hepatitis A virus (HAV), particularly among people who inject drugs (PWID), continue across the United States and globally. However, the herd immunity threshold and vaccination coverage required to prevent outbreaks are unknown. We used surveillance data and dynamic modeling to estimate herd immunity thresholds among PWID in 16 US states. We used a previously published dynamic model of HAV transmission calibrated to surveillance data from outbreaks involving PWID in 16 states. Using state-level calibrated models, we estimated the basic reproduction number (R0) and herd immunity threshold for PWID in each state. We performed a meta-analysis of herd immunity thresholds to determine the critical vaccination coverage required to prevent most HAV outbreaks among PWID. Estimates of R0 for HAV infection ranged from 2.2 (95% confidence interval [CI], 1.9-2.5) for North Carolina to 5.0 (95% CI, 4.5-5.6) for West Virginia. Corresponding herd immunity thresholds ranged from 55% (95% CI, 47%-61%) for North Carolina to 80% (95% CI, 78%-82%) for West Virginia. Based on the meta-analysis, we estimated a pooled herd immunity threshold of 64% (95% CI, 61%-68%; 90% prediction interval, 52%-76%) among PWID. Using the prediction interval upper bound (76%) and assuming 95% vaccine efficacy, we estimated that vaccination coverage of 80% could prevent most HAV outbreaks. Hepatitis A vaccination programs in the United States may need to achieve vaccination coverage of at least 80% among PWID in order to prevent most HAV outbreaks among this population.

Transmission dynamics and effect of control measures on the 2022 outbreak of mpox among gay, bisexual, and other men who have sex with men in England: a mathematical modelling study

The 2022 global outbreak of mpox (formerly known as monkeypox) spread primarily among gay, bisexual, and other men who have sex with men (GBMSM), with the initial cluster being identified in England in May, 2022. Understanding its epidemiological characteristics and the reasons for its downturn in July, 2022, will help to control future outbreaks. We collated data for all diagnosed mpox cases (3621) from England from May 1, 2022, to Nov 16, 2022. Data from 75 individuals with mpox allowed estimation of the incubation period, while data from 121 case-contact pairs were used to estimate the serial interval. Six methods, including a structured dynamic compartmental transmission model, were used to estimate the basic reproduction number (R0). The structured model assumed all male individuals with mpox were GBMSM, who were then stratified into subgroups for those at low risk and high risk for mpox. This best fitting model was used to estimate the reduction in transmissibility, and the effective infectious period (before isolating), that resulted in the outbreak downturn, and the effect of vaccination initiated from June 27, 2022. Bayesian methods were used for parameter estimation and model calibration. Most cases occurred in men (3544 of 3621, 97·9%). The median incubation period for mpox was 6·90 days (95% credible interval [CrI] 4·08-20·21), and the serial interval was 8·82 days (5·22-25·81). R0 estimates ranged from 1·41 to 2·17. The structured transmission model estimated that 83·8% of infections (95% CrI 83·5-85·3) resulted from sexual partnerships with GBMSM individuals at high risk of mpox. The outbreak downturn probably resulted from a 44·5% reduction in the sexual partner rate among all GBMSM (24·9-55·8) and 20·0% reduction in the effective infectious period (4·1-33·9), preventing 165 896 infections (115 584-217 730). Vaccination marginally increased the number of infections prevented (166 081, 115 745-217 947), but minimised a resurgence in cases from January, 2023, and could have averted four times more infections if initiated earlier. Our findings were sensitive to assumptions regarding the vaccine's effectiveness and the GBMSM subgroup at high risk of mpox. The mpox outbreak in England probably resulted from high sexual partner rates among some GBMSM, with reductions in partner rates reversing the outbreak, and with vaccination minimising future outbreaks. National Institute for Health Research (UK).

Estimation of the Galactocentric Distance of the Sun from Cepheids Close to the Solar Circle

Based on Cepheids located near the solar circle, we have determined the Galactocentric distance of the Sun R0 and the Galactic rotation velocity at the solar distance V0. For our analysis we used a sample of -200 classical Cepheids from the catalogue by Skowron et al. (2019), where the distances to them were determined from the period–luminosity relation. For these stars the proper motions and line-of-sight velocities were taken from the Gaia DR3 catalogue. The values of R0 found lie within the range 7.8–8.3 kpc, depending on the heliocentric distance of the sample stars, on the adopted solar velocity relative to the local standard of rest, and on whether or not the perturbations caused by the Galactic spiral density wave are taken into account. The dispersion of the R0 estimates is -2 kpc. Similarly, the values of V0 lie within the range 240–270 km s-1 with a dispersion of the estimates of 70–90 km s-1. We consider the following estimates to be the final ones: R0=8,24+0,20 kpc and V0=268+8 km s-1 found by taking into account the perturbations from the Galactic spiral density wave.

Anchoring the mean generation time in the SEIR to mitigate biases in ℜ0 estimates due to uncertainty in the distribution of the epidemiological delays.

The basic reproduction number, , is of paramount importance in the study of infectious disease dynamics. Primarily, serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating .

Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R0 for the Republic of Panama in the 1999-2022 period

Nowadays, the ability to make data-driven decisions in public health is of utmost importance. To achieve this, it is necessary for modelers to comprehend the impact of models on the future state of healthcare systems. Compartmental models are a valuable tool for making informed epidemiological decisions, and the proper parameterization of these models is crucial for analyzing epidemiological events. This work evaluated the use of compartmental models in conjunction with Particle Swarm Optimization (PSO) to determine optimal solutions and understand the dynamics of Dengue epidemics. The focus was on calculating and evaluating the rate of case reproduction, R0, for the Republic of Panama. Three compartmental models were compared: Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected-Recovered (SEIR), and Susceptible-Infected-Recovered Human-Susceptible-Infected Vector (SIR Human-SI Vector, SIR-SI). The models were informed by demographic data and Dengue incidence in the Republic of Panama between 1999 and 2022, and the susceptible population was analyzed. The SIR, SEIR, and SIR-SI models successfully provided R0 estimates ranging from 1.09 to 1.74. This study provides, to the best of our understanding, the first calculation of R0 for Dengue outbreaks in the Republic of Panama.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R 0 ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R 0 values were consistently lower (mean≤1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean≥1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R 0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p=0.46) between the birth-death model and surveillance R 0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R 0 estimation and surveillance-based R 0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Estimation of a Within-Herd Transmission Rate for African Swine Fever in Vietnam

We describe results from a panel study in which pigs from a 17-sow African swine fever (ASF) positive herd in Thái Bình province, Vietnam, were followed over time to record the date of onset of ASF signs and the date of death from ASF. Our objectives were to (1) fit a susceptible-exposed-infectious-removed disease model to the data with transmission coefficients estimated using approximate Bayesian computation; (2) provide commentary on how a model of this type might be used to provide decision support for disease control authorities. For the outbreak in this herd, the median of the average latent period was 10 days (95% HPD (highest posterior density interval): 2 to 19 days), and the median of the average duration of infectiousness was 3 days (95% HPD: 2 to 4 days). The estimated median for the transmission coefficient was 3.3 (95% HPD: 0.4 to 8.9) infectious contacts per ASF-infectious pig per day. The estimated median for the basic reproductive number, R0, was 10 (95% HPD: 1.1 to 30). Our estimates of the basic reproductive number R0 were greater than estimates of R0 for ASF reported previously. The results presented in this study may be used to estimate the number of pigs expected to be showing clinical signs at a given number of days following an estimated incursion date. This will allow sample size calculations, with or without adjustment to account for less than perfect sensitivity of clinical examination, to be used to determine the appropriate number of pigs to examine to detect at least one with the disease. A second use of the results of this study would be to inform the equation-based within-herd spread components of stochastic agent-based and hybrid simulation models of ASF.

Macroenvironmental interactions as driving indicators for detecting tetracycline resistance spread among A. hydrophila exposure to environmentally relevant oxytetracycline levels

There have been efforts dedicating to investigating the effect of high levels of oxytetracycline (OTC) on tetracycline (TC) resistance among zoonotic pathogen Aeromonas hydrophila (AH), an antibiotic-resistant bacterial indicator. However, the dynamic behavior of TC-resistant AH in response to environmentally relevant OTC concentrations at a population-level has not been fully understood. Here we developed a bacterial population dynamic model to quantify TC-resistant AH posed by OTC-dependent resistance selection pressure. The key environmental factors known as water temperature, water activity, and pH were incorporated into model to produce pattern-oriented simulation outcomes. We estimated resistance acquisition number (R0) and showed that R0 was >1 at water temperature <26 °C, indicating coexistence of resistant- and susceptible-AH. Sensitivity tests revealed that cell density-dependent conjugation rate indicated crucial for influencing R0 estimation. Our results also indicated that maximum fraction of TC-resistant AH was mostly affected by temperature/activity in water and increased with increasing of OTC concentrations. We estimated OTC concentrations causing 50 % maximum fold-change of TC-resistant AH fraction ranging from 7 to 19 µg/L. Our findings suggest that control of TC resistance in AH requires particularly attention to water with temperature 26 °C lower, water activity 0.95 higher with pH ∼ 5–8. Our mechanistic framework provides a useful tool-kit to improve our understanding of the critical role of OTC stress-induced macroenvironmental interactions in a TC resistance − AH system and highlights the potential for antimicrobial management to promote resilience in aquatic ecosystems.

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis.

Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.

Monkeypox: Early estimation of basic reproduction number R0 in Europe.

This world outbreak of Monkeypox (MPX) infections outside Africa emerged on May 2022 in Europe and spread worldwide with unique characteristics: inter-human contagion and infection in men without specific previous immunization, prevalently men-who-have-sex-with-men(MSM). Phylogenetic analysis confirmed a unique clade, the West African clade, subclade IIb. On August 30, WHO stated 48 895 laboratory confirmed cases from 101 different countries, of which 28 050 were in Europe. It has therefore become important to define new epidemiological indices. Starting from our new surveillance system EpiMPX open data, we defined an early R0 measure, using European ECDC confirmed cases from the epidemic start to the end of August 2022; our early R0 pooled median is 2.44, with high variability between countries. We observed the higher R0 in Portugal and Germany, followed by Italy, Spain, and France. Anyway, these high estimates refer to the MSM group rather than to the general population. Early estimation of R0 can be used to support the epidemiological understanding of transmission dynamics and contain MPX from spreading in naive populations and core groups with risk factors. MPX is in an evolving situation with much to learn and to do to combat the current epidemic outbreak.

Estimation of R0 for the spread of SARS-CoV-2 in Germany from excess mortality

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.

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

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.

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

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

A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating [formula omitted] and social distancing behaviour

We investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R0 for each CSA. Values of R0 ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index cflatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found cflatten to be more influential in the clustering process than R0. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R0 itself.