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Infectious diseases: Household modeling with missing data
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Importance
Understanding the susceptibility and infectiousness of children and adolescents in comparison to adults is important to appreciate their role in the COVID-19 pandemic.
Objective
To determine SARS-CoV-2 susceptibility and infectiousness of children and adolescents with adults as comparator for three variants (wild-type, alpha, delta) in the household setting. We aimed to identify the effects independent of vaccination or prior infection.
Data sources
We searched EMBASE, PubMed and medRxiv up to January 2022.
Study selection
Two reviewers independently identified studies providing secondary household attack rates (SAR) for SARS-CoV-2 infection in children (0–9 years), adolescents (10–19 years) or both compared with adults (20 years and older).
Data extraction and synthesis
Two reviewers independently extracted data, assessed risk of bias and performed a random-effects meta-analysis model.
Main outcomes and measures
Odds ratio (OR) for SARS-CoV-2 infection comparing children and adolescents with adults stratified by wild-type (ancestral type), alpha, and delta variant, respectively. Susceptibility was defined as the secondary attack rate (SAR) among susceptible household contacts irrespective of the age of the index case. Infectiousness was defined as the SAR irrespective of the age of household contacts when children/adolescents/adults were the index case.
Results
Susceptibility analysis: We included 27 studies (308,681 contacts), for delta only one (large) study was available. Compared to adults, children and adolescents were less susceptible to the wild-type and delta, but equally susceptible to alpha. Infectiousness analysis: We included 21 studies (201,199 index cases). Compared to adults, children and adolescents were less infectious when infected with the wild-type and delta. Alpha -related infectiousness remained unclear, 0–9 year old children were at least as infectious as adults. Overall SAR among household contacts varied between the variants.
Conclusions and relevance
When considering the potential role of children and adolescents, variant-specific susceptibility, infectiousness, age group and overall transmissibility need to be assessed.
Background
Household is potentially the highest-risk exposure setting of SARS-COV-2 transmission, in which the role of children has remained controversial.
Methods
Through retrieval in PubMed and EMBASE, studies were included in two parts: meta-analysis of the household secondary attack rate (SAR) and case analysis of household pediatric infections.
Results
A total of 95 articles were included: 48 for meta-analysis and 47 for case analysis. Pediatric COVID-19 only comprised a minority of the household transmission. The total pooled household SAR of child index cases and contacts were 0.20 (95% CI: 0.15-0.26) and 0.24 (95% CI: 0.18-0.30). Lower household transmissibility was reported in both child index cases and contacts compared to adults (RR = 0.64, 95% CI: 0.50-0.81; RR=0.74, 95% CI: 0.64-0.85). Younger children were as susceptible as the older children (RR=0.89, 95% CI: 0.72-1.10). Through subgroup analyses of different variants and periods, increased household SAR was observed in children (Wild: 0.20; Alpha: 0.42; Delta: 0.35; Omicron: 0.56) and no significant difference was found in household SAR between children and adults when new variants dominated.
Conclusions
Although children were demonstrated not to be dominant in the household transmission, their transmissibility of SARS-CoV-2 appeared on the rise as new variants emerge.
Importance:
An overall household secondary attack rate (SAR) of 18.9% (95% CI, 16.2%-22.0%) through June 17, 2021 was previously reported for SARS-CoV-2. Emerging variants of concern and increased vaccination have affected transmission rates.
Objective:
To evaluate how reported household SARs changed over time and whether SARs varied by viral variant and index case and contact vaccination status.
Data sources:
PubMed and medRxiv from June 18, 2021, through March 8, 2022, and reference lists of eligible articles. Preprints were included.
Study selection:
Articles with original data reporting the number of infected and total number of household contacts. Search terms included SARS-CoV-2, COVID-19, variant, vaccination, secondary attack rate, secondary infection rate, household, index case, family contacts, close contacts, and family transmission.
Data extraction and synthesis:
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline was followed. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95% CIs.
Main outcomes and measures:
SAR stratified by covariates according to variant, index case and contact vaccination status, and index case identification period. SARs were used to estimate vaccine effectiveness on the basis of the transmission probability for susceptibility to infection (VES,p), infectiousness given infection (VEI,p), and total vaccine effectiveness (VET,p).
Results:
Household SARs were higher for 33 studies with midpoints in 2021 to 2022 (37.3%; 95% CI, 32.7% to 42.1%) compared with 63 studies with midpoints through April 2020 (15.5%; 95% CI, 13.2% to 18.2%). Household SARs were 42.7% (95% CI, 35.4% to 50.4%) for Omicron (7 studies), 36.4% (95% CI, 33.4% to 39.5%) for Alpha (11 studies), 29.7% (95% CI, 23.0% to 37.3%) for Delta (16 studies), and 22.5% (95% CI, 18.6% to 26.8%) for Beta (3 studies). For full vaccination, VES,p was 78.6% (95% CI, 76.0% to 80.9%) for Alpha, 56.4% (95% CI, 54.6% to 58.1%) for Delta, and 18.1% (95% CI, -18.3% to 43.3%) for Omicron; VEI,p was 75.3% (95% CI, 69.9% to 79.8%) for Alpha, 21.9% (95% CI, 11.0% to 31.5%) for Delta, and 18.2% (95% CI, 0.6% to 32.6%) for Omicron; and VET,p was 94.7% (95% CI, 93.3% to 95.8%) for Alpha, 64.4% (95% CI, 58.0% to 69.8%) for Delta, and 35.8% (95% CI, 13.0% to 52.6%) for Omicron.
Conclusions and relevance:
These results suggest that emerging SARS-CoV-2 variants of concern have increased transmissibility. Full vaccination was associated with reductions in susceptibility and infectiousness, but more so for Alpha than Delta and Omicron. The changes in estimated vaccine effectiveness underscore the challenges of developing effective vaccines concomitant with viral evolution.
Background
Understanding the factors that affect the transmissibility of SARS-CoV-2 remains important to keep transmission low and maximize the health benefits of vaccination. We assessed the factors associated with the transmissibility of SARS-CoV-2 based on contact tracing data.
Methods
From 1 October to 9 December 2020, 29,385 laboratory-confirmed SARS-CoV-2 cases (index cases, i.e. the first identified laboratory-confirmed cases or with the earliest symptom onset in a setting) and 64,608 traced contacts were identified in Greece. We assessed the prevalence of symptoms in cases, calculated secondary attack rates and assessed factors associated with infectivity and susceptibility to infection.
Results
There were 11,232 contacts secondarily infected (secondary attack rate: 17.4%, 95% CI:17.0–17.8). Contacts aged 0–11 and 12–17 years were less susceptible to infection than adults 65 years or older (odds ratio (OR) [95% CI]: 0.28 [0.26–0.32] and 0.44 [0.40–0.49], respectively). Index cases aged 65 years or older were more likely to infect their contacts than other adults or children/adolescents. The odds of infection [95% CI] were higher in contacts exposed within the household (1.71 [1.59–1.85] vs. other) and in cases with cough (1.17 [1.11–1.25] vs. no cough). There was an interaction between the age of the index and the age of the contact with contacts 65 years or older having a higher probability of infection when exposed to cases of similar age than to children.
Conclusions
Our findings highlight the role of age and age mixing in infectivity and susceptibility to SARS-CoV-2 infection. Precautions are necessary for individuals 65 or older as they have higher infectivity and susceptibility in contact with their peers.
Importance
A previous systematic review and meta-analysis of household transmission of SARS-CoV-2 that summarized 54 published studies through October 19, 2020, found an overall secondary attack rate (SAR) of 16.6% (95% CI, 14.0%-19.3%). However, the understanding of household secondary attack rates for SARS-CoV-2 is still evolving, and updated analysis is needed.
Objective
To use newly published data to further the understanding of SARS-CoV-2 transmission in the household.
Data Sources
PubMed and reference lists of eligible articles were used to search for records published between October 20, 2020, and June 17, 2021. No restrictions on language, study design, time, or place of publication were applied. Studies published as preprints were included.
Study Selection
Articles with original data that reported at least 2 of the following factors were included: number of household contacts with infection, total number of household contacts, and secondary attack rates among household contacts. Studies that reported household infection prevalence (which includes index cases), that tested contacts using antibody tests only, and that included populations overlapping with another included study were excluded. Search terms were SARS-CoV-2 or COVID-19 with secondary attack rate, household, close contacts, contact transmission, contact attack rate, or family transmission.
Data Extraction and Synthesis
Meta-analyses were performed using generalized linear mixed models to obtain SAR estimates and 95% CIs. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed.
Main Outcomes and Measures
Overall household SAR for SARS-CoV-2, SAR by covariates (contact age, sex, ethnicity, comorbidities, and relationship; index case age, sex, symptom status, presence of fever, and presence of cough; number of contacts; study location; and variant), and SAR by index case identification period.
Results
A total of 2722 records (2710 records from database searches and 12 records from the reference lists of eligible articles) published between October 20, 2020, and June 17, 2021, were identified. Of those, 93 full-text articles reporting household transmission of SARS-CoV-2 were assessed for eligibility, and 37 studies were included. These 37 new studies were combined with 50 of the 54 studies (published through October 19, 2020) from our previous review (4 studies from Wuhan, China, were excluded because their study populations overlapped with another recent study), resulting in a total of 87 studies representing 1 249 163 household contacts from 30 countries. The estimated household SAR for all 87 studies was 18.9% (95% CI, 16.2%-22.0%). Compared with studies from January to February 2020, the SAR for studies from July 2020 to March 2021 was higher (13.4% [95% CI, 10.7%-16.7%] vs 31.1% [95% CI, 22.6%-41.1%], respectively). Results from subgroup analyses were similar to those reported in a previous systematic review and meta-analysis; however, the SAR was higher to contacts with comorbidities (3 studies; 50.0% [95% CI, 41.4%-58.6%]) compared with previous findings, and the estimated household SAR for the B.1.1.7 (α) variant was 24.5% (3 studies; 95% CI, 10.9%-46.2%).
Conclusions and Relevance
The findings of this study suggest that the household remains an important site of SARS-CoV-2 transmission, and recent studies have higher household SAR estimates compared with the earliest reports. More transmissible variants and vaccines may be associated with further changes.
One of the significant unanswered questions about COVID-19 epidemiology relates to the role of children in transmission. This study uses data on infections within households in order to estimate the susceptibility and infectivity of children compared to those of adults. The data were collected from households in the city of Bnei Brak, Israel, in which all household members were tested for COVID-19 using PCR (637 households, average household size of 5.3). In addition, serological tests were performed on a subset of the individuals in the study. Inspection of the PCR data shows that children are less likely to be tested positive compared to adults (25% of children positive over all households, 44% of adults positive over all households, excluding index cases), and the chance of being positive increases with age. Analysis of joint PCR/serological data shows that there is under-detection of infections in the PCR testing, which is more substantial in children. However, the differences in detection rates are not sufficient to account for the differences in PCR positive rates in the two age groups. To estimate relative transmission parameters, we employ a discrete stochastic model of the spread of infection within a household, allowing for susceptibility and infectivity parameters to differ among children and adults. The model is fitted to the household data using a simulated maximum likelihood approach. To adjust parameter estimates for under-detection of infections in the PCR results, we employ a multiple imputation procedure using estimates of under-detection in children and adults, based on the available serological data. We estimate that the susceptibility of children (under 20 years old) is 43% (95% CI: [31%, 55%]) of the susceptibility of adults. The infectivity of children was estimated to be 63% (95% CI: [37%, 88%]) relative to that of adults.
Importance
Crowded indoor environments, such as households, are high-risk settings for the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Objectives
To examine evidence for household transmission of SARS-CoV-2, disaggregated by several covariates, and to compare it with other coronaviruses.
Data Source
PubMed, searched through October 19, 2020. Search terms included SARS-CoV-2 or COVID-19 with secondary attack rate, household, close contacts, contact transmission, contact attack rate, or family transmission.
Study Selection
All articles with original data for estimating household secondary attack rate were included. Case reports focusing on individual households and studies of close contacts that did not report secondary attack rates for household members were excluded.
Data Extraction and Synthesis
Meta-analyses were done using a restricted maximum-likelihood estimator model to yield a point estimate and 95% CI for secondary attack rate for each subgroup analyzed, with a random effect for each study. To make comparisons across exposure types, study was treated as a random effect, and exposure type was a fixed moderator. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed.
Main Outcomes and Measures
Secondary attack rate for SARS-CoV-2, disaggregated by covariates (ie, household or family contact, index case symptom status, adult or child contacts, contact sex, relationship to index case, adult or child index cases, index case sex, number of contacts in household) and for other coronaviruses.
Results
A total of 54 relevant studies with 77 758 participants reporting household secondary transmission were identified. Estimated household secondary attack rate was 16.6% (95% CI, 14.0%-19.3%), higher than secondary attack rates for SARS-CoV (7.5%; 95% CI, 4.8%-10.7%) and MERS-CoV (4.7%; 95% CI, 0.9%-10.7%). Household secondary attack rates were increased from symptomatic index cases (18.0%; 95% CI, 14.2%-22.1%) than from asymptomatic index cases (0.7%; 95% CI, 0%-4.9%), to adult contacts (28.3%; 95% CI, 20.2%-37.1%) than to child contacts (16.8%; 95% CI, 12.3%-21.7%), to spouses (37.8%; 95% CI, 25.8%-50.5%) than to other family contacts (17.8%; 95% CI, 11.7%-24.8%), and in households with 1 contact (41.5%; 95% CI, 31.7%-51.7%) than in households with 3 or more contacts (22.8%; 95% CI, 13.6%-33.5%).
Conclusions and Relevance
The findings of this study suggest that given that individuals with suspected or confirmed infections are being referred to isolate at home, households will continue to be a significant venue for transmission of SARS-CoV-2.
Aim
The coronavirus disease 2019 (COVID‐19) pandemic has affected hundreds of thousands of people. Data on symptoms and prognoses in children are rare.
Methods
A systematic literature review was carried out to identify papers on COVID‐19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), using the Medline and EMBASE databases between 1 January and 18 March 2020.
Results
The search identified 45 relevant scientific papers and letters. The review showed that children have so far accounted for 1‐5% of diagnosed COVID‐19 cases, they often have milder disease than adults and deaths have been extremely rare. Diagnostic findings have been similar to adults, with fever and respiratory symptoms being prevalent, but fewer children seem to have developed severe pneumonia. Elevated inflammatory markers were less common in children and lymphocytopenia seemed rare. Newborn infants have developed symptomatic COVID‐19, but evidence of vertical intrauterine transmission was scarce. Suggested treatment included providing oxygen, inhalations, nutritional support and maintaining fluids and electrolyte balances.
Conclusions
COVID‐19 has occurred in children, but they seemed to have a milder disease course and better prognoses than adults. Deaths were extremely rare.
Ryacas is an R (R Core Team, 2018) package that enables a
computer algebra system (CAS) within R via the open source CAS
yacas (A. Z. Pinkus & Winitzki, 2002; A. Pinkus, Winnitzky, & Mazur,
2016), which is short for “yet another computer algebra
system”. Ryacas includes both a high-level (symbol) interface
using R objects like matrices and vectors as well as direct
access to the underlying yacas such that the user can use the
full yacas system, including for example defining new summation
rules.
Background
When an outbreak of a novel pathogen occurs, some of the most pressing questions from a public-health point of view relate to its transmissibility, and the probabilities of different clinical outcomes following infection, to allow an informed response. Estimates of these quantities are often based on household data due to the high potential for transmission in this setting, but typically a rich spectrum of individual-level outcomes (from uninfected to serious illness) are simplified to binary data (infected or not). We address the added benefit from retaining the heterogeneous outcome information in the case of the 2009-10 influenza pandemic, which posed particular problems for estimation of key epidemiological characteristics due to its relatively mild nature and hence low case ascertainment rates.
Methods
We use mathematical models of within-household transmission and case ascertainment, together with Bayesian statistics to estimate transmission probabilities stratified by household size, the variability of infectiousness of cases, and a set of probabilities describing case ascertainment. This novel approach was applied to data we collected from the early "containment phase" stage of the epidemic in Birmingham, England. We also conducted a comprehensive review of studies of household transmission of influenza A(H1N1)pdm09.
Results
We find large variability in the published estimates of within-household transmissibility of influenza A(H1N1)pdm09 in both model-based studies and those reporting secondary attack rates, finding that these estimates are very sensitive to how an infected case is defined. In particular, we find that reliance on laboratory confirmation alone underestimates the true number of cases, while utilising the heterogeneous range of outcomes (based on case definitions) for household infections allows a far more comprehensive pattern of transmission to be elucidated.
Conclusions
Differences in household sizes and how cases are defined could account for an appreciable proportion of the reported variability of within-household transmissibility of influenza A(H1N1)pdm09. Retaining and statistically analysing the full spectrum of individual-level outcomes (based on case definitions) rather than taking a potentially arbitrary threshold for infection, provides much-needed additional information. In a future pandemic, our approach could be used as a real-time analysis tool to infer the true number of cases, within-household transmission rates and levels of case ascertainment.
We propose a transmission model to estimate the main characteristics of influenza transmission in households. The model details the risks of infection in the household and in the community at the individual scale. Heterogeneity among subjects is investigated considering both individual susceptibility and infectiousness. The model was applied to a data set consisting of the follow‐up of influenza symptoms in 334 households during 15 days after an index case visited a general practitioner with virologically confirmed influenza.
Estimating the parameters of the transmission model was challenging because a large part of the infectious process was not observed: only the dates when new cases were detected were observed. For each case, the data were augmented with the unobserved dates of the start and the end of the infectious period. The transmission model was included in a 3‐levels hierarchical structure: (i) the observation level ensured that the augmented data were consistent with the observed data, (ii) the transmission level described the underlying epidemic process, (iii) the prior level specified the distribution of the parameters. From a Bayesian perspective, the joint posterior distribution of model parameters and augmented data was explored by Markov chain Monte Carlo (MCMC) sampling.
The mean duration of influenza infectious period was estimated at 3.8 days (95 per cent credible interval, 95 per cent CI [3.1,4.6]) with a standard deviation of 2.0 days (95 per cent CI [1.1,2.8]). The instantaneous risk of influenza transmission between an infective and a susceptible within a household was found to decrease with the size of the household, and established at 0.32 person day ⁻¹ (95 per cent CI [0.26,0.39]); the instantaneous risk of infection from the community was 0.0056day ⁻¹ (95 per cent CI [0.0029,0.0087]). Focusing on the differences in transmission between children (less than 15 years old) and adults, we estimated that the former were more likely to transmit than adults ( posterior probability larger than 99 per cent), but that the mean duration of the infectious period was similar in children (3.6 days, 95 per cent CI [2.3,5.2]) and adults (3.9 days, 95 per cent CI [3.2,4.9]). The posterior probability that children had a larger community risk was 76 per cent and the posterior probability that they were more susceptible than adults was 79 per cent. Copyright © 2004 John Wiley & Sons, Ltd.
Objective:
To evaluate the secondary attack rate (SAR) in children and adolescents, contacts of essential activities workers who were infected by SARS-CoV-2; and to describe associated clinical and epidemiological data.
Methods:
A cross-sectional study conducted in children and adolescents aged 5 to 19 years of age, that were household contacts of parents and other relatives who were infected by SARS-CoV-2 in the city of Goiânia, Central Brazil, from March to October 2020. Sociodemographic and clinical data were collected from all participants. Nasopharyngeal and oropharyngeal swabs were collected and tested for SARS-CoV-2 RNA using real-time reverse transcription polymerase chain reaction (RT-PCR). Factors associated with SARS-CoV-2 infection and SAR were analyzed using Poisson regression.
Results:
A total of 267 children and adolescents were investigated. The prevalence of SARS-CoV-2 RNA by the real-time RT-PCR test and/or the presence of COVID-19 associated symptoms (anosmia/ageusia and flu syndrome) was 25.1% (95.0% Confidence Interval [95.0% CI] = 20.3-30.6). More than half (55.1%) of the participants had sygns and symptoms. The most prevalent signs and symptoms in positive individuals were nasal congestion (62.7%), headache (55.2%), cough (50.8%), myalgia (47.8%), runny nose (47.8%), and anosmia (47.8%). The Poisson model showed that the following signs or symptoms were associated with SARS-CoV-2 infection: fever, nasal congestion, decreased appetite, nausea, anosmia, and ageusia. Families that had more than one infected adult, in addition to the index case, presented greater transmissibility to children and adolescents.
Conclusions:
Our results contribute to the hypothesis that children and adolescents are not important sources of transmission of SARS-CoV-2 in the home environment during a period of social distancing and school closure; even though they are susceptible to infection in the household (around ¼ of our study population).
Importance:
The degree to which children and adolescents are infected by and transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. The role of children and adolescents in transmission of SARS-CoV-2 is dependent on susceptibility, symptoms, viral load, social contact patterns, and behavior.
Objective:
To systematically review the susceptibility to and transmission of SARS-CoV-2 among children and adolescents compared with adults.
Data sources:
PubMed and medRxiv were searched from database inception to July 28, 2020, and a total of 13 926 studies were identified, with additional studies identified through hand searching of cited references and professional contacts.
Study selection:
Studies that provided data on the prevalence of SARS-CoV-2 in children and adolescents (younger than 20 years) compared with adults (20 years and older) derived from contact tracing or population screening were included. Single-household studies were excluded.
Data extraction and synthesis:
PRISMA guidelines for abstracting data were followed, which was performed independently by 2 reviewers. Quality was assessed using a critical appraisal checklist for prevalence studies. Random-effects meta-analysis was undertaken.
Main outcomes and measures:
Secondary infection rate (contact-tracing studies) or prevalence or seroprevalence (population screening studies) among children and adolescents compared with adults.
Results:
A total of 32 studies comprising 41 640 children and adolescents and 268 945 adults met inclusion criteria, including 18 contact-tracing studies and 14 population screening studies. The pooled odds ratio of being an infected contact in children compared with adults was 0.56 (95% CI, 0.37-0.85), with substantial heterogeneity (I2 = 94.6%). Three school-based contact-tracing studies found minimal transmission from child or teacher index cases. Findings from population screening studies were heterogenous and were not suitable for meta-analysis. Most studies were consistent with lower seroprevalence in children compared with adults, although seroprevalence in adolescents appeared similar to adults.
Conclusions and relevance:
In this meta-analysis, there is preliminary evidence that children and adolescents have lower susceptibility to SARS-CoV-2, with an odds ratio of 0.56 for being an infected contact compared with adults. There is weak evidence that children and adolescents play a lesser role than adults in transmission of SARS-CoV-2 at a population level. This study provides no information on the infectivity of children.
S ummary
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
The Longini-Koopman model (1982, Biometrics 38, 115-126) describes the process underlying the transmission of an infectious disease in terms of household and community level transmission probabilities. This model is generalized by allowing for different transmission probabilities that may correspond to various levels of risk factors on both the household and community levels. Two types of models are considered: (i) models for household data, where the numbers of susceptible and infected members in each household are known along with the values of household level risk factors; and (ii) models for individual data, where the infection status and risk factor level are known for each individual in the household. Although the type (i) models can be expressed as special cases of the type (ii) models, they deserve special attention as they can be represented and analyzed as log-linear models. Both types of models can be analyzed using maximum likelihood methods, while the type (i) models, when expressed as log-linear models, can also be analyzed by the weighted least squares method. Data from influenza epidemics in Tecumseh, Michigan and Seattle, Washington are used to illustrate these methods.
A model is devised for the distribution of the total number of cases in households from a homogeneous community. In the model, community-acquired infection serves as a source of initial infection within households as well as of possible further cases. In addition, infected household members can infect others in the household. Maximum likelihood procedures for the model parameters are given. The model is fitted to symptom data on influenza and the common cold. Influenza seems to spread more easily in the community than within the household, while the opposite may be the case for the common cold. The model, which does not require specification of the time of onset of infection for individuals, can be fitted to serological data; this would provide a more accurate measure of household infection than the symptom data used.
The analysis of infectious disease data presents challenges arising from the dependence in the data and the fact that only part of the transmission process is observable. These difficulties are usually overcome by making simplifying assumptions. The paper explores the use of Markov chain Monte Carlo (MCMC) methods for the analysis of infectious disease data, with the hope that they will permit analyses to be made under more realistic assumptions. Two important kinds of data sets are considered, containing temporal and non-temporal information, from outbreaks of measles and influenza. Stochastic epidemic models are used to describe the processes that generate the data. MCMC methods are then employed to perform inference in a Bayesian context for the model parameters. The MCMC methods used include standard algorithms, such as the Metropolis–Hastings algorithm and the Gibbs sampler, as well as a new method that involves likelihood approximation. It is found that standard algorithms perform well in some situations but can exhibit serious convergence difficulties in others. The inferences that we obtain are in broad agreement with estimates obtained by other methods where they are available. However, we can also provide inferences for parameters which have not been reported in previous analyses.