Estimate Transmission Parameters Research Articles

A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China.

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal-spatial-mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases.

Within-herd transmission of Mycoplasma bovis infections after initial detection in dairy cows

Mycoplasma bovis outbreaks in cattle, including pathogen spread between age groups, are not well understood. Our objective was to estimate within-herd transmission across adult dairy cows, youngstock and calves. Results from 3 tests (PCR, ELISA, and culture) per cow and 2 tests (PCR and ELISA) per youngstock and calf were utilized in an age-stratified Susceptible-Infected-Removed/Recovered (SIR) model to estimate within-herd transmission parameters, pathways, and potential effects of farm management practices. A cohort of adult cows, youngstock and calves on 20 Dutch dairy farms with a clinical outbreak of M. bovis in adult cows were sampled, with collection of blood, conjunctival fluid and milk from cows, and blood and conjunctival fluid from calves and youngstock, 5 times over a timespan of 12 wk. Any individual with at least 1 positive laboratory test was considered M. bovis-positive. Transmission dynamics were modeled using an age-stratified SIR model featuring 3 age strata. Associations with farm management practices were explored using Fisher's Exact tests and Poisson regression. Estimated transmission parameters were highly variable among herds and cattle-age groups. Notably, transmission from cows to cows, youngstock, or to calves was associated with R-values ranging from 1.0 to 80 secondarily infected cows per herd, 1.2 to 38 secondarily infected youngstock per herd, and 0.1 to 91 secondarily infected calves per herd, respectively. In case of transmission from youngstock to youngstock, calves or to cows, R-values were 0.7 to 96 secondarily infected youngstock per herd, 1.1 to 76 secondarily infected calves per herd, and 0.1 to 107 secondarily infected cows per herd. For transmission from calves to calves, youngstock or to cows, R-values were 0.5 to 60 secondarily infected calves per herd, 1.1 to 41 secondarily infected youngstock per herd, and 0.1 to 47 secondarily infected cows per herd. Among on-farm transmission pathways, cow-to-youngstock, cow-to-calf and cow-to-cow were identified as most significant contributors, with calf-to-calf and calf-to-youngstock also having noteworthy roles. Youngstock-to-youngstock was also implicated, albeit to a lesser extent. Whereas the primary focus was a clinical outbreak of M. bovis among adult dairy cows, it was evident that transmission extended to calves and youngstock, contributing to overall spread. Factors influencing transmission and specific transmission pathways were associated with internal biosecurity (separate caretakers for various age groups, number of people involved), external biosecurity (contractors, external employees), as well as indirect transmission routes (number of feed and water stations).

Estimating transmission dynamics of African swine fever virus from experimental studies.

African swine fever virus (ASFV) continues to spread across the world, and currently, there are no treatments or vaccines available to combat this virus. Reliable estimates of transmission parameters for ASFV are therefore needed to establish effective contingency plans. This study used data from controlled ASFV inoculations of pigs to assess the transmission parameters. Three models were developed with (binary, piecewise-linear and exponential) time-dependent levels of infectiousness based on latency periods of 3-5 days derived from the analysis of 294 ethylenediamine tetraacetic acid-stabilized blood samples originating from 16 pigs with direct and 10 pigs with indirect contact to 8 inoculated pigs. The models were evaluated for three different discrete latency periods of infection. The likelihood ratio test showed that a binary model had an equally good fit for a latency period of 4 or 5 days as the piecewise-linear and exponential model. However, for a latency period of 3 days, the piecewise-linear and exponential models had the best fit. The modelling was done in discrete time as testing was conducted on specific days. The main contribution of this study is the estimation of ASFV genotype II transmission through the air in a confined space. The estimated transmission parameters via air are not much lower than for direct contact between pigs. The estimated parameters should be useful for future simulations of control measures against ASFV.

Epidemiological and genomic findings of the first documented Italian outbreak of SARS-CoV-2 Alpha variant of concern

From 24 December 2020 to 8 February 2021, 163 cases of SARS-CoV-2 Alpha variant of concern (VOC) were identified in Chieti province, Abruzzo region. Epidemiological data allowed the identification of 14 epi-clusters. With one exception, all the epi-clusters were linked to the town of Guardiagrele: 149 contacts formed the network, two-thirds of which were referred to the family/friends context. Real data were then used to estimate transmission parameters. According to our method, the calculated Re(t) was higher than 2 before the 12 December 2020. Similar values were obtained from other studies considering Alpha VOC. Italian sequence data were combined with a random subset of sequences obtained from the GISAID database. Genomic analysis showed close identity between the sequences from Guardiagrele, forming one distinct clade. This would suggest one or limited unspecified viral introductions from outside to Abruzzo region in early December 2020, which led to the diffusion of Alpha VOC in Guardiagrele and in neighbouring municipalities, with very limited inter-regional mixing.

The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models.

Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March–June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number () remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number () varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

Mechanistic modelling of African swine fever: A systematic review

The spread of African swine fever (ASF) poses a grave threat to the global swine industry. Without an available vaccine, understanding transmission dynamics is essential for designing effective prevention, surveillance, and intervention strategies. These dynamics can often be unraveled through mechanistic modelling. To examine the assumptions on transmission and objectives of the mechanistic models of ASF, a systematic review of the scientific literature was conducted. Articles were examined across multiple epidemiological and model characteristics, with filiation between models determined through the creation of a neighbor-joined tree using phylogenetic software.Thirty-four articles qualified for inclusion, with four main modelling objectives identified: estimating transmission parameters (11 studies), assessing determinants of transmission (7), examining consequences of hypothetical outbreaks (5), assessing alternative control strategies (11). Population-based (17), metapopulation (5), and individual-based (12) model frameworks were represented, with population-based and metapopulation models predominantly used among domestic pigs, and individual-based models predominantly represented among wild boar. The majority of models (25) were parameterized to the genotype II isolates currently circulating in Europe and Asia.Estimated transmission parameters varied widely among ASFV strains, locations, and transmission scale. Similarly, parameter assumptions between models varied extensively. Uncertainties on epidemiological and ecological parameters were usually accounted for to assess the impact of parameter values on the modelled infection trajectory. To date, almost all models are host specific, being developed for either domestic pigs or wild boar despite the fact that spillover events between domestic pigs and wild boar are evidenced to play an important role in ASF outbreaks. Consequently, the development of more models incorporating such transmission routes is crucial. A variety of codified and hypothetical control strategies were compared however they were all a priori defined interventions. Future models, built to identify the optimal contributions across many control methods for achieving specific outcomes should provide more useful information for policy-makers. Further, control strategies were examined in competition with each other, which is opposed to how they would actually be synergistically implemented. While comparing strategies is beneficial for identifying a rank-order efficacy of control methods, this structure does not necessarily determine the most effective combination of all available strategies. In order for ASFV models to effectively support decision-making in controlling ASFV globally, these modelling limitations need to be addressed.

Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups

To date, few studies of parasite epidemiology have investigated ‘who acquires infection from whom’ in wildlife populations. Nonetheless, identifying routes of disease transmission within a population, and determining the key groups of individuals that drive parasite transmission and maintenance, are fundamental to understanding disease dynamics. Gammaherpesviruses are a widespread group of DNA viruses that infect many vertebrate species, and murine gammaherpesviruses (i.e. MuHV-4) are a standard lab model for studying human herpesviruses, for which much about the pathology and immune response elicited to infection is well understood. However, despite this extensive research effort, primarily in the lab, the transmission route of murine gammaherpesviruses within their natural host populations is not well understood. Here, we aimed to understand wood mouse herpesvirus (WMHV) transmission, by fitting a series of population dynamic models to field data on wood mice naturally infected with WMHV and then estimating transmission parameters within and between demographic groups of the host population. Different models accounted for different combinations of host sex (male/female), age (subadult/adult) and transmission functions (density/frequency-dependent). We found that a density-dependent transmission model incorporating explicit sex groups fitted the data better than all other proposed models. Male-to-male transmission was the highest among all possible combinations of between- and within-sex transmission classes, suggesting that male behaviour is a key factor driving WMHV transmission. Our models also suggest that transmission between sexes, although important, wasn’t symmetrical, with infected males playing a significant role in infecting naïve females but not vice versa. Overall this work shows the power of coupling population dynamic models with long-term field data to elucidate otherwise unobservable transmission processes in wild disease systems.

Estimating Transmission Parameters for COVID-19 Clusters by Using Symptom Onset Data, Singapore, January-April 2020.

We estimated the generation interval distribution for coronavirus disease on the basis of serial intervals of observed infector–infectee pairs from established clusters in Singapore. The short mean generation interval and consequent high prevalence of presymptomatic transmission requires public health control measures to be responsive to these characteristics of the epidemic.

Chopping the tail: How preventing superspreading can help to maintain COVID-19 control

Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a stochastic compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find that the effective reproduction number (RE) dropped below 1 rapidly in all five locations following social distancing orders in mid-March, 2020, but that gradually increasing mobility starting around mid-April led to an RE once again above 1 in late May (Los Angeles, Miami, and Atlanta) or early June (Santa Clara County and Seattle). However, we find that increased social distancing starting in mid-July in response to epidemic resurgence once again dropped RE below 1 in all locations by August 14. We next used the fitted model to ask: how does truncating the individual-level transmission rate distribution (which removes periods of time with especially high individual transmission rates and thus models superspreading events) affect epidemic dynamics and control? We find that interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing.

Mathematical Quantification of Transmission in Experiments: FMDV Transmission in Pigs Can Be Blocked by Vaccination and Separation.

Quantitative understanding of transmission with and without control measures is important for the control of infectious diseases because it helps to determine which of these measures (or combinations thereof) will be effective to reduce transmission. In this paper, the statistical methods used to estimate transmission parameters are explained. To show how these methods can be used we reviewed literature for papers describing foot-and-mouth disease virus (FMDV) transmission in pigs and we used the data to estimate transmission parameters. The analysis showed that FMDV transmits very well when pigs have direct contact. Transmission, however, is reduced when a physical barrier separates infected and susceptible non-vaccinated pigs. Vaccination of pigs can prevent infection when virus is administered by a single intradermal virus injection in the bulb of the heel, but it cannot prevent infection when pigs are directly exposed to either non-vaccinated or vaccinated FMDV infected pigs. Physical separation combined with vaccination is observed to block transmission. Vaccination and separation can make a significant difference in the estimated number of new infections per day. Experimental transmission studies show that the combined effect of vaccination and physical separation can significantly reduce transmission (R < 1), which is a very relevant result for the control of between-farm transmission.

Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques.

BackgroundCompartmental models dominate epidemic modeling. Transmission parameters between compartments are typically estimated through stochastic parameterization processes that depends on detailed statistics of transmission characteristics, which are economically and resource-wise expensive to collect.ObjectiveWe aim to apply deep learning techniques as a lower data dependency alternative to estimate transmission parameters of a customized compartmental model, for the purpose of simulating the dynamics of the US coronavirus disease (COVID-19) epidemic and projecting its further development.MethodsWe constructed a compartmental model and developed a multistep deep learning methodology to estimate the model’s transmission parameters. We then fed the estimated transmission parameters to the model to predict development of the US COVID-19 epidemic for 35 and 42 days. Epidemics are considered suppressed when the basic reproduction number (R0) is less than 1.ResultsThe deep learning–enhanced compartmental model predicts that R0 will fall to <1 around August 17-19, 2020, at which point the epidemic will effectively start to die out, and that the US “infected” population will peak around August 16-18, 2020, at 3,228,574 to 3,308,911 individual cases. The model also predicted that the number of accumulative confirmed cases will cross the 5 million mark around August 7, 2020.ConclusionsCurrent compartmental models require stochastic parameterization to estimate the transmission parameters. These models’ effectiveness depends upon detailed statistics on transmission characteristics. As an alternative, deep learning techniques are effective in estimating these stochastic parameters with greatly reduced dependency on data particularity.

Estimating Transmission Parameters for Respiratory Syncytial Virus and Predicting the Impact of Maternal and Pediatric Vaccination.

Respiratory syncytial virus (RSV) is a leading cause of respiratory tract illness in young children and a major cause of hospital admissions globally. Here we fit age-structured transmission models with immunity propagation to data from the Netherlands (2012-2017). Data included nationwide hospitalizations with confirmed RSV, general practitioner (GP) data on attendance for care from acute respiratory infection, and virological testing of acute respiratory infections at the GP. The transmission models, equipped with key parameter estimates, were used to predict the impact of maternal and pediatric vaccination. Estimates of the basic reproduction number were generally high (R0 > 10 in scenarios with high statistical support), while susceptibility was estimated to be low in nonelderly adults (<10% in persons 20-64 years) and was higher in older adults (≥65 years). Scenario analyses predicted that maternal vaccination reduces the incidence of infection in vulnerable infants (<1 year) and shifts the age of first infection from infants to young children. Pediatric vaccination is expected to reduce the incidence of infection in infants and young children (0-5 years), slightly increase incidence in 5 to 9-year-old children, and have minor indirect benefits.

Reliable OFDM Data Transmission with Pilot Tones and Error-Correction Coding in Shallow Underwater Acoustic Channel

The performance of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Horizontal, shallow-water channels are characterised by extremely disadvantageous transmission properties, due to strong multipath propagation and refraction phenomena. The paper presents the results of communication tests performed during a shallow, inland-water experiment with the use of a laboratory model of a UAC system implementing the Orthogonal Frequency-Division Multiplexing (OFDM) technique. The physical layer of data transmission is partially configurable, enabling adaptation of the modulation and channel coding parameters to the specific propagation conditions. The communication tests were preceded by measurement of the UAC channel transmission properties. Based on the estimated transmission parameters, four configurations of OFDM modulation parameters were selected, and for each of them, communication tests were performed with the use of two Error-Correction Coding (ECC) techniques. In each case, the minimum coding rate was determined for which reliable data transmission with a Bit Error Rate (BER) of less than 10 − 4 is possible.

Dynamics and Prediction of the COVID-19 Epidemics in the U.S. – A Compartmental Model with Deep Learning Enhancement

Compartmental models dominate epidemic modeling. Estimations of transmission parameters between compartments are typically done through stochastic parameterization processes that depend upon detailed statistics on transmission characteristics, which are economically and resource-wide expensive to collect. We apply deep learning techniques as a lower data dependency alternative to estimating transmission parameters of a customized compartmental model, for the purposes of projecting further development of the US COVID-19 epidemics. The deep learning-enhanced compartment model predicts that the basic reproduction rate (R0 ) will become less than one around June 22–27, 2020, and that the infection transmission parameter will drop to virtually zero around July 14, 2020, implying that the total number of confirmed cases will likely become stabilized around that timeframe (projected at 2·4 million). Funding Statement: None. Declaration of Interests: Authors declare no competing interests.

A novel sub-epidemic modeling framework for short-term forecasting epidemic waves

BackgroundSimple phenomenological growth models can be useful for estimating transmission parameters and forecasting epidemic trajectories. However, most existing phenomenological growth models only support single-peak outbreak dynamics whereas real epidemics often display more complex transmission trajectories.MethodsWe develop and apply a novel sub-epidemic modeling framework that supports a diversity of epidemic trajectories including stable incidence patterns with sustained or damped oscillations to better understand and forecast epidemic outbreaks. We describe how to forecast an epidemic based on the premise that the observed coarse-scale incidence can be decomposed into overlapping sub-epidemics at finer scales. We evaluate our modeling framework using three outbreak datasets: Severe Acute Respiratory Syndrome (SARS) in Singapore, plague in Madagascar, and the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC) and four performance metrics.ResultsThe sub-epidemic wave model outperforms simpler growth models in short-term forecasts based on performance metrics that account for the uncertainty of the predictions namely the mean interval score (MIS) and the coverage of the 95% prediction interval. For example, we demonstrate how the sub-epidemic wave model successfully captures the 2-peak pattern of the SARS outbreak in Singapore. Moreover, in short-term sequential forecasts, the sub-epidemic model was able to forecast the second surge in case incidence for this outbreak, which was not possible using the simple growth models. Furthermore, our findings support the view that the national incidence curve of the Ebola epidemic in DRC follows a stable incidence pattern with periodic behavior that can be decomposed into overlapping sub-epidemics.ConclusionsOur findings highlight how overlapping sub-epidemics can capture complex epidemic dynamics, including oscillatory behavior in the trajectory of the epidemic wave. This observation has significant implications for interpreting apparent noise in incidence data where the oscillations could be dismissed as a result of overdispersion, rather than an intrinsic part of the epidemic dynamics. Unless the oscillations are appropriately modeled, they could also give a false positive, or negative, impression of the impact from public health interventions. These preliminary results using sub-epidemic models can help guide future efforts to better understand the heterogenous spatial and social factors shaping sub-epidemic patterns for other infectious diseases.

A highly invasive chimeric ranavirus can decimate tadpole populations rapidly through multiple transmission pathways

A consequence of genetic recombination can be the evolution of highly virulent pathogen strains. Virulence can manifest through various mechanisms of host–pathogen interaction that facilitate transmission. We discovered a highly virulent chimeric ranavirus in Georgia, USA, estimated transmission parameters using controlled experiments, and developed compartmental disease models to examine potential consequences on tadpoles of a susceptible host species (Lithobates sylvaticus). Our models included three transmission pathways: direct transmission via host contact, environmental transmission via shed virions in water, and transmission via necrophagy of morbid individuals. Unlike previous models, we categorized individuals into multiple stages of infection (susceptible, latency, and infectious), where the probability of disease-induced mortality increased throughout the duration of infection following a gamma distribution with an integer shape parameter. Our simulations showed that accounting for pathogen incubation improved model predictions when compared to survival data from controlled experiments. We found that transmission due to direct contact of tadpoles was the dominant transmission pathway; however, environmental transmission played a larger role as tadpole density increased and the epidemic progressed. Estimated R0 (basic reproduction number) values >570 for all transmission pathways indicate that targeting only one transmission pathway is unlikely to thwart invasion. Additionally, the high invasion potential and diseased-induced mortality associated with this chimeric ranavirus indicate that this pathogen is a substantial threat to amphibian biodiversity in the United States.

Serological Epidemiology of Foot-and-mouth Disease among Sedentary Mixed-species Herds in Adamawa Region, Cameroon

Foot-and-mouth disease (FMD) is an economically important viral disease of domestic animals and wildlife. High circulation occurs during outbreaks but little is known about the current transmission dynamics in sedentary cattle and sheep herds. To investigate on this, samples from cattle and sheep located in the Vina Division of the Adamawa region of Cameroon, during FMD outbreaks were collected. Non-Structural Protein (NSP) and antigen detecting/serotyping FMD ELISAs were used for sample screening. The NSP serological data of cattle was used to estimate transmission parameters in catalytic and reverse catalytic models. The Akaike’s Information Criteria (AIC) revealed the reverse catalytic model as the most parsimonious for the NSP serological data and was used to estimate the force of infection (FOI), the rate of waning immunity and to estimate historic periods of sustained transmission. Four serological types of FMD notably O, A, SAT 1 and SAT 2 were identified from cattle vesicular epithelia tissues. Seroprevalence findings revealed 65.14% and 15.71% FMDV antibodies in cattle and sheep respectively with highest prevalence in both populations occurring in Mbidjoro. The FOI (λ) in sedentary herds was constant; the rate of waning immunity (ω) was 0.32 meaning cattle are generally immune for 3.12 years post natural infection. The reproductive number (Rt) was 7.33, meaning approximately 87% of cattle always need to be effectively immunized to prevent outbreaks. Therefore, FMD circulates in sedentary cattle and sheep populations in the study area with four serological types detected in cattle.

The 2017 plague outbreak in Madagascar: Data descriptions and epidemic modelling

From August to November 2017, Madagascar endured an outbreak of plague. A total of 2417 cases of plague were confirmed, causing a death toll of 209. Public health intervention efforts were introduced and successfully stopped the epidemic at the end of November. The plague, however, is endemic in the region and occurs annually, posing the risk of future outbreaks. To understand the plague transmission, we collected real-time data from official reports, described the outbreak's characteristics, and estimated transmission parameters using statistical and mathematical models. The pneumonic plague epidemic curve exhibited multiple peaks, coinciding with sporadic introductions of new bubonic cases. Optimal climate conditions for rat flea to flourish were observed during the epidemic. Estimate of the plague basic reproduction number during the large wave of the epidemic was high, ranging from 5 to 7 depending on model assumptions. The incubation and infection periods for bubonic and pneumonic plague were 4.3 and 3.4 days and 3.8 and 2.9 days, respectively. Parameter estimation suggested that even with a small fraction of the population exposed to infected rat fleas (1/10,000) and a small probability of transition from a bubonic case to a secondary pneumonic case (3%), the high human-to-human transmission rate can still generate a large outbreak. Controlling rodent and fleas can prevent new index cases, but managing human-to-human transmission is key to prevent large-scale outbreaks.

Maximum likelihood estimation of influenza vaccine effectiveness against transmission from the household and from the community.

Influenza vaccination is recommended as the best way to protect against influenza infection and illness. Due to seasonal changes in influenza virus types and subtypes, a new vaccine must be produced, and vaccine effectiveness (VE) must be estimated, annually. Since 2010, influenza vaccination has been recommended universally in the United States, making randomized clinical trials unethical. Recent studies have used a monitored household cohort study design to determine separate VE estimates against influenza transmission from the household and community. We developed a probability model and accompanying maximum likelihood procedure to estimate vaccine-related protection against transmission of influenza from the household and the community. Using agent-based stochastic simulations, we validated that we can obtain maximum likelihood estimates of transmission parameters and VE close to their true values. Sensitivity analyses to examine the effect of deviations from our assumptions were conducted. We used our method to estimate transmission parameters and VE from data from a monitored household study in Michigan during the 2012-2013 influenza season and were able to detect a significant protective effect of influenza vaccination against community-acquired transmission.

Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation.

SummaryMortality data are routinely collected for many livestock and poultry species, and they are often used for epidemiological purposes, including estimating transmission parameters. In this study, we infer transmission rates for African swine fever virus (ASFV), an important transboundary disease of swine, using mortality data collected from nine pig herds in the Russian Federation with confirmed outbreaks of ASFV. Parameters in a stochastic model for the transmission of ASFV within a herd were estimated using approximate Bayesian computation. Estimates for the basic reproduction number varied amongst herds, ranging from 4.4 to 17.3. This was primarily a consequence of differences in transmission rate (range: 0.7–2.2), but also differences in the mean infectious period (range: 4.5–8.3 days). We also found differences amongst herds in the mean latent period (range: 5.8–9.7 days). Furthermore, our results suggest that ASFV could be circulating in a herd for several weeks before a substantial increase in mortality is observed in a herd, limiting the usefulness of mortality data as a means of early detection of an outbreak. However, our results also show that mortality data are a potential source of data from which to infer transmission parameters, at least for diseases which cause high mortality.