A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

INTRODUCTION

Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention.

METHODS

A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data.

RESULTS

PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis.

CONCLUSIONS

The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study

The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of new variants is critical for timely optimisation of control strategies. We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to rapidly explore spatial and temporal variations in the estimated transmission advantage. We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29 (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Delta is 1.77 (95% CrI 1.69-1.85) times more transmissible than Alpha (England data). Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.

A practical guide to mathematical methods for estimating infectious disease outbreak risks

Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Effect of the incremental protection of previous infection against Omicron infection among individuals with a hybrid of infection- and vaccine-induced immunity: a population-based cohort study in Canada

OBJECTIVES

We examined the incremental protection and durability of infection-acquired immunity against Omicron infection in individuals with hybrid immunity in Ontario, Canada.

METHODS

We followed up 6 million individuals with at least one multiplex reverse transcriptase-polymerase chain reaction test before November 21, 2021, until an Omicron infection. Protection via infection-acquired immunity was assessed by comparing Omicron infection risk between previously infected individuals and those without documented infection under different vaccination scenarios and stratified by time since the last infection or vaccination.

RESULTS

A previous infection was associated with 68% (95% CI 61-73) and 43% (95% CI 27-56) increased protection against Omicron infection in individuals with two and three doses, respectively. Among individuals with two-dose vaccination, the incremental protection of infection-induced immunity decreased from 79% (95% CI 75-81) within 3 months after vaccination or infection to 27% (95% CI 14-37) at 9-11 months. In individuals with three-dose vaccination, it decreased from 57% (95% CI 50-63) within 3 months to 37% (95% CI 19-51) at 3-5 months after vaccination or infection.

CONCLUSION

Previous SARS-CovV-2 infections provide added cross-variant immunity to vaccination. Given the limited durability of infection-acquired protection in individuals with hybrid immunity, its influence on shield-effects at the population level and reinfection risks at the individual level may be limited.

Estimated incidence and transmission intensity of rubella infection in Zambia pre-vaccine era 2005-2016

The rubella disease burden in Zambia may be under-estimated. Using models, we describe the transmission dynamics, determine the incidence estimates and assess the level of underestimation of the real burden of rubella infection in Zambia during the pre-vaccination period 2005-2016. This study used both the deterministic compartmental model and likelihood-based method using a Bayesian framework to describe the epidemiology of rubella. A total of 1313 cases of rubella were confirmed with the highest annual number of 255 new cases recorded in 2008. However, 2014 recorded the highest monthly median positivity rate of 9.0%. The observed median rubella cases were 5.5. There was a seasonal pattern in the occurrence of laboratory-confirmed rubella, with higher test positivity rates of rubella infection usually recorded in the months of September, October and November. The modelled monthly median incidence of rubella infection among the general population was 76 and 20 among pregnant women. The incidence of rubella among the non-pregnant women was 44. The average effective reproductive number (Rt) between 2005 and 2016 was estimated as 1.2 with the peak of infection occurring in 2016. The measles surveillance system underestimates the observed burden of rubella. A mass vaccination campaign conducted between January and July is recommended.

The impact of universal varicella vaccination on the clinical burden of varicella in Colombia: A National database Analysis, 2008-2019

In 2015, one-dose universal varicella vaccination (UVV) was introduced in the Colombian National Immunization Program targeting children aged 12 months, expanding to a two-dose program in 2019. This study aimed to examine the effect of one-dose UVV on the burden of varicella in Colombia. A retrospective study was conducted using national databases to estimate incidence and mortality for the target (1-4 years old), non-target (less than 1 and 5 years and older) and overall (all age groups) populations from the pre-UVV period (January 2008-June 2015) to the post-UVV period (July 2015-December 2019). A time-series analyses with ARIMA modeling was used to project expected varicella incidence and mortality in the absence of UVV in the post-UVV period. UVV impact was estimated by comparing predicted and observed values, providing point estimates and prediction intervals (PI). Overall vaccination coverage rate was over 90 % from 2016-2019. Following UVV introduction, mean annual incidence rates reduced from 743.6 to 676.8 per 100,000 in the target population and from 203.2 to 198.1 per 100,000 in the overall population. Our study estimated a reduction in varicella incidence from 2017, with the highest reduction of 70.5 % (95 % PI: 78.2-54.2) and 54.8 % (95 % PI: 65.0-36.4) observed in 2019 for the target and the overall populations, respectively. The ARIMA model estimated UVV in Colombia to have prevented 198,236 varicella cases from 2015 to 2019. Mortality reduced in the overall population from 0.8 per 1,000,000 to 0.5 per 1,000,000 and from 1.3 per 1,000,000 to 0.5 per 1,000,000 in the target population, in the pre-UVV and post-UVV periods, respectively. However, these differences were not statistically significant. Our study showed a significant reduction in varicella incidence after implementation of a one-dose UVV program in Colombia, increasing over time. Further assessment is needed to evaluate the impact of a two-dose UVV program in Colombia.

Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study

During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.

Urinary lead level and colonization by antibiotic resistant bacteria: Evidence from a population-based study

BACKGROUND

Infection by antibiotic resistant bacteria (ARB) is a global health crisis and asymptomatic colonization increases risk of infection. Nonhuman studies have linked heavy metal exposure to the selection of ARB; however, few epidemiologic studies have examined this relationship. This study analyzes the association between urinary lead level and colonization by ARB in a nonclinical human population.

METHODS

Data came from the Survey of the Health of Wisconsin 2016-2017, and its ancillary Wisconsin Microbiome Study. Urinary lead levels, adjusted for creatinine, were used to assess exposure. ARB included methicillin resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), fluoroquinolone resistant Gram-negative bacilli (RGNB), and Clostridium difficile (C. diff), from skin, nose, and mouth swabs, and saliva and stool samples. Logistic regression, adjusted for covariates, was used to evaluate associations between Pb and ARB. Secondary analysis investigated Pb resistance from ARB isolates.

RESULTS

Among 695 participants, 239 (34%) tested positive for ARB. Geometric mean urinary Pb (unadjusted) was 0.286 µg/L (95% confidence intervals [CI] = 0.263, 0.312) for ARB negative participants and 0.323 µg/L (95% CI = 0.287, 0.363) for ARB positive participants. Models adjusted for demographics, diet, and antibiotic use showed elevated odds of positive colonization for those in the 95th percentile (vs. below) of Pb exposure (odds ratio [OR] = 2.05, 95% CI = 0.95, 4.44), and associations were highest in urban residents (OR = 2.85, 95% CI = 1.07, 7.59). RGNB isolates were most resistant to Pb.

DISCUSSION

These novel results suggest that Pb exposure is associated with increased colonization by ARB, and that RGNB are particularly resistant to Pb.

A wind speed threshold for increased outdoor transmission of coronavirus: an ecological study

BACKGROUND

To examine whether outdoor transmission may contribute to the COVID-19 epidemic, we hypothesized that slower outdoor wind speed is associated with increased risk of transmission when individuals socialize outside.

METHODS

Daily COVID-19 incidence reported in Suffolk County, NY, between March 16th and December 31st, 2020, was the outcome. Average wind speed and maximal daily temperature were collated by the National Oceanic and Atmospheric Administration. Negative binomial regression was used to model incidence rates while adjusting for susceptible population size.

RESULTS

Cases were very high in the initial wave but diminished once lockdown procedures were enacted. Most days between May 1st, 2020, and October 24th, 2020, had temperatures 16-28 °C and wind speed diminished slowly over the year and began to increase again in December 2020. Unadjusted and multivariable-adjusted analyses revealed that days with temperatures ranging between 16 and 28 °C where wind speed was < 8.85 km per hour (KPH) had increased COVID-19 incidence (aIRR = 1.45, 95% C.I. = [1.28-1.64], P < 0.001) as compared to days with average wind speed ≥ 8.85 KPH.

CONCLUSION

Throughout the U.S. epidemic, the role of outdoor shared spaces such as parks and beaches has been a topic of considerable interest. This study suggests that outdoor transmission of COVID-19 may occur by noting that the risk of transmission of COVID-19 in the summer was higher on days with low wind speed. Outdoor use of increased physical distance between individuals, improved air circulation, and use of masks may be helpful in some outdoor environments where airflow is limited.

Modeling of network based digital contact tracing and testing strategies, including the pre-exposure notification system, for the COVID-19 pandemic

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.

Estimation of the incubation period of COVID-19 using viral load data

The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases’ recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.

Knowledge, attitude and practice (KAP) survey of canine rabies in Khyber Pakhtunkhwa and Punjab Province of Pakistan

BACKGROUND

This study aimed to assess the extent of knowledge and understanding of rabies disease in rural and urban communities of Pakistan. It also identified malpractices after suspected dog bite that might pose a risk for humans contracting rabies.

METHODS

A cross-sectional study was conducted (n = 1466) on people having different age groups and educational levels in four different geographic regions of Punjab and Khyber Pakhtunkhwa provinces in Pakistan. Knowledge, attitude, and practices of people were assessed using a structured questionnaire. We used a bivariate and multivariate analysis to study the association between rabies related mortalities in near or extended family members and different risk behaviors.

RESULTS

Our results demonstrate that the majority of the juvenile population (less than 18 years of age) were not aware of the clinical signs of rabies in animals. 75% of the total respondents were not vaccinated against rabies, 60% did not seek a doctor's advice after a suspected animal bite, and 55% had inadequate health care facilities for rabies patients in local hospitals. Respondents that had pets at home had not vaccinated (38%; p < 0.05; odds ratio 1.58) themselves against rabies due to lack of knowledge and awareness of pre-exposure prophylaxis for rabies (51%; p < 0.05; odds ratio 1.25). They also tend to not visit doctor after suspected bite (52%; p < 0.05; odds ratio 1.97), which may had resulted in more deaths (65%; p < 0.05; odds ratio 1.73) of someone in their near or extended family due to rabies.

CONCLUSIONS

Lack of knowledge about the nature of rabies disease and prophylaxis has contributed to increase of rabies related deaths. Inadequate health care facilities and poor attitude of not seeking medical attention after suspected dog bite are the major reasons of rabies related deaths. These findings could help in devising a targeted management strategy and awareness program to control and reduce the incidence of human rabies related deaths in Pakistan.

The Impact of Pre-exposure Prophylaxis for Human Immunodeficiency Virus on Gonorrhea Prevalence

Pre-exposure prophylaxis (PrEP) has been shown to be highly effective in reducing the risk of HIV infection in gay and bisexual men who have sex with men (GbMSM). However, PrEP does not protect against other sexually transmitted infections (STIs). In some populations, PrEP has also led to riskier behavior such as reduced condom usage, with the result that the prevalence of bacterial STIs like gonorrhea has increased. Here, we develop a compartmental model of the transmission of HIV and gonorrhea and the impacts of PrEP, condom usage, STI testing frequency and potential changes in sexual risk behavior stemming from the introduction of PrEP in a population of GbMSM. We find that introducing PrEP causes an increase in gonorrhea prevalence for a wide range of parameter values, including at the currently recommended frequency of STI testing once every three months for individuals on PrEP. Moreover, the model predicts that a higher STI testing frequency alone is not enough to prevent a rise in gonorrhea prevalence, unless the testing frequency is increased to impractical levels. However, testing every 2 months in combination with a 10-25 % reduction in risky behavior by individuals on PrEP would maintain gonorrhea prevalence at pre-PrEP levels. The results emphasize that programs making PrEP more available should be accompanied by efforts to support condom usage and frequent STI testing, in order to avoid an increase in the prevalence of gonorrhea and other bacterial STIs.

[Self-reported infections in the German National Cohort (GNC) in the context of the current research landscape]

BACKGROUND

Infectious diseases continue to play an important role for disease perception, health-economic considerations and public health in Germany. In recent years, infectious diseases have been linked to the development of non-communicable diseases. Analyses of the German National Cohort (GNC) may provide deeper insights into this issue and pave the way for new targeted approaches in disease prevention.

OBJECTIVES

The aim was to describe the tools used to assess infectious diseases and to present initial data on infectious disease frequencies, as well as to relate the GNC assessment tools to data collection methods in other studies in Germany.

METHODS

As part of the baseline examination, questions regarding infectious diseases were administered using both an interview and a self-administered touchscreen questionnaire. Data from the initial 101,787 GNC participants were analysed.

RESULTS

In the interview, 0.2% (HIV/AIDS) to 8.6% (shingles) of respondents reported ever having a medical diagnosis of shingles, postherpetic neuralgia (in cases where shingles was reported), hepatitis B/C, HIV/AIDS, tuberculosis or sepsis if treated in hospital. In the questionnaire, 12% (cystitis) to 81% (upper respiratory tract infections) of respondents reported having experienced at least one occurrence of upper or lower respiratory tract infections, gastrointestinal infections, cystitis or fever within the past 12 months.

OUTLOOK

The cross-sectional analyses of data and tools presented here - for example on determinants of susceptibility to self-reported infections - can be anticipated from the year 2021 onward. Beyond that, more extensive research into infectious disease epidemiology will follow, particularly once analyses of GNC biological materials have been performed.

Respiratory syncytial virus hospitalisations among young children: a data linkage study

We aimed to provide comprehensive estimates of laboratory-confirmed respiratory syncytial virus (RSV)-associated hospitalisations. Between 2012 and 2015, active surveillance of acute respiratory infection (ARI) hospitalisations during winter seasons was used to estimate the seasonal incidence of laboratory-confirmed RSV hospitalisations in children aged <5 years in Auckland, New Zealand (NZ). Incidence rates were estimated by fine age group, ethnicity and socio-economic status (SES) strata. Additionally, RSV disease estimates determined through active surveillance were compared to rates estimated from hospital discharge codes. There were 5309 ARI hospitalisations among children during the study period, of which 3923 (73.9%) were tested for RSV and 1597 (40.7%) were RSV-positive. The seasonal incidence of RSV-associated ARI hospitalisations, once corrected for non-testing, was 6.1 (95% confidence intervals 5.8–6.4) per 1000 children <5 years old. The highest incidence was among children aged <3 months. Being of indigenous Māori or Pacific ethnicity or living in a neighbourhood with low SES independently increased the risk of an RSV-associated hospitalisation. RSV hospital discharge codes had a sensitivity of 71% for identifying laboratory-confirmed RSV cases. RSV infection is a leading cause of hospitalisation among children in NZ, with significant disparities by ethnicity and SES. Our findings highlight the need for effective RSV vaccines and therapies.

Meningococcal carriage by age in the African meningitis belt: a systematic review and meta-analysis

Meningococcal carriage dynamics drive patterns of invasive disease. The distribution of carriage by age has been well described in Europe, but not in the African meningitis belt, a region characterised by frequent epidemics of meningitis. We aimed to estimate the age-specific prevalence of meningococcal carriage by season in the African meningitis belt. We searched PubMed, Web of Science, the Cochrane Library and grey literature for papers reporting carriage of Neisseria meningitidis in defined age groups in the African meningitis belt. We used a mixed-effects logistic regression to model meningococcal carriage prevalence as a function of age, adjusting for season, location and year. Carriage prevalence increased from low prevalence in infants (0.595% in the rainy season, 95% CI 0.482–0.852%) to a broad peak at age 10 (1.94%, 95% CI 1.87–2.47%), then decreased in adolescence. The odds of carriage were significantly increased during the dry season (OR 1.5 95% CI 1.4–1.7) and during outbreaks (OR 6.7 95% CI 1.6–29). Meningococcal carriage in the African meningitis belt peaks at a younger age compared to Europe. This is consistent with contact studies in Africa, which show that children 10–14 years have the highest frequency of contacts. Targeting older children in Africa for conjugate vaccination may be effective in reducing meningococcal transmission.

Analysis of influenza data generated by four epidemiological surveillance laboratories in Mexico, 2010-2016

The disease caused by the influenza virus is a global public health problem due to its high rates of morbidity and mortality. Thus, analysis of the information generated by epidemiological surveillance systems has vital importance for health decision making. A retrospective analysis was performed using data generated by the four molecular diagnostic laboratories of the Mexican Social Security Institute between 2010 and 2016. Demographics, influenza positivity, seasonality, treatment choices and vaccination status analyses were performed for the vaccine according to its composition for each season. In all cases, both the different influenza subtypes and different age groups were considered separately. The circulation of A/H1N1pdm09 (48.7%), influenza A/H3N2 (21.1%), influenza B (12.6%), influenza A not subtyped (11%) and influenza A/H1N1 (6.6%) exhibited well-defined annual seasonality between November and March, and there were significant increases in the number of cases every 2 years. An inadequate use of oseltamivir was determined in 38% of cases, and the vaccination status in general varied between 12.1 and 18.5% depending on the season. Our results provide current information about influenza in Mexico and demonstrate the need to update both operational case definitions and medical practice guidelines to reduce the inappropriate use of antibiotics and antivirals.

Understanding the intersection of race and place: the case of tuberculosis in Michigan

BACKGROUND

Race and place intersect to produce location-based variation in disease distributions. We analyzed the geographic distribution of tuberculosis (TB) incidence in Michigan, USA to better understand the complex interplay between race and place, comparing patterns in Detroit, Wayne County and the state of Michigan as a whole.

METHODS

Using cross-sectional TB surveillance data from the Michigan Department of Health and Human Services, multivariable statistical models were developed to analyze the residence patterns of TB incidence from 2007 through 2012. Two-way interactions among the residence location and race of cases were assessed.

RESULTS

Overall, Detroit residents experienced 58% greater TB incidence than residents of Wayne County or the state of Michigan. Racial inequalities were less pronounced in Detroit compared to both Wayne County and the state of Michigan. Blacks in Detroit had 2.01 times greater TB incidence than Whites, while this inequality was 3.62 times more in Wayne County and 8.72 greater in the state of Michigan.

CONCLUSION

Our results highlight how race and place interact to influence patterns of TB disease, and the ways in which this interaction is context dependent. TB elimination in the U.S. will require strategies that address the local social environment, as much as the physical environment.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Rare mutations in Pfmdr1 gene of Plasmodium falciparum detected in clinical isolates from patients treated with anti-malarial drug in Nigeria

Background

Plasmodium falciparum, the deadliest causative agent of malaria, has high prevalence in Nigeria. Drug resistance causing failure of previously effective drugs has compromised anti-malarial treatment. On this basis, there is need for a proactive surveillance for resistance markers to the currently recommended artemisinin-based combination therapy (ACT), for early detection of resistance before it become widespread.

Methods

This study assessed anti-malarial resistance genes polymorphism in patients with uncomplicated P. falciparum malaria in Lagos, Nigeria. Sanger and Next Generation Sequencing (NGS) methods were used to screen for mutations in thirty-seven malaria positive blood samples targeting the P. falciparum chloroquine-resistance transporter (Pfcrt), P. falciparum multidrug-resistance 1 (Pfmdr1), and P. falciparum kelch 13 (Pfk13) genes, which have been previously associated with anti-malarial resistance.

Results

Expectedly, the NGS method was more proficient, detecting six Pfmdr1, seven Pfcrt and three Pfk13 mutations in the studied clinical isolates from Nigeria, a malaria endemic area. These mutations included rare Pfmdr1 mutations, N504K, N649D, F938Y and S967N, which were previously unreported. In addition, there was moderate prevalence of the K76T mutation (34.6%) associated with chloroquine and amodiaquine resistance, and high prevalence of the N86 wild type allele (92.3%) associated with lumefantrine resistance.

Conclusion

Widespread circulation of mutations associated with resistance to current anti-malarial drugs could potentially limit effective malaria therapy in endemic populations.

Assessment of West nile virus transmission risk from a weather-dependent epidemiological model and a global sensitivity analysis framework

West Nile virus (WNV) transmission risk is strongly related to weather conditions due to the sensitivity of the mosquitoes to climatic factors. We assess the WNV transmission risk of humans to seasonal weather conditions and the relative effects of parameters affecting the transmission dynamics. The assessment involves a known epidemiological model we extend to account for temperature and precipitation and a global uncertainty and sensitivity analysis framework. We focus on three relevant quantities, the basic reproduction number (R₀), the minimum infection rate (MIR), and the number of infected individuals. The highest-priority weather-related WNV transmission risks can be attributed to the birth and death rate of mosquitoes, the biting rate of mosquitoes to birds, and the probability of transmission from birds to mosquitoes. Global sensitivity analysis indicates that these parameters make up a big part of the explained variance in R₀ and MIR. The analysis allows for a dynamic assessment over time capturing the period parameters are more relevant than others. Global uncertainty and sensitivity analysis of WNV transmission risk to humans enable insights into the relative importance of individual parameters of the transmission cycle of the virus facilitating the understanding of the dynamics and the implementation of tailored control strategies.

Improving measles incidence inference using age-structured serological data

Measles is a target for elimination in all six WHO regions by 2020, and over the last decade, there has been considerable progress towards this goal. Surveillance is recognised as a cornerstone of elimination programmes, allowing early identification of outbreaks, thus enabling control and preventing re-emergence. Fever–rash surveillance is increasingly available across WHO regions, and this symptom-based reporting is broadly used for measles surveillance. However, as measles control increases, symptom-based cases are increasingly likely to reflect infection with other diseases with similar symptoms such as rubella, which affects the same populations, and can have a similar seasonality. The WHO recommends that cases from suspected measles outbreaks be laboratory-confirmed, to identify ‘true’ cases, corresponding to measles IgM titres exceeding a threshold indicative of infection. Although serological testing for IgM has been integrated into the fever–rash surveillance systems in many countries, the logistics of sending in every suspected case are often beyond the health system's capacity. We show how age data from serologically confirmed cases can be leveraged to infer the status of non-tested samples, thus strengthening the information we can extract from symptom-based surveillance. Applying an age-specific confirmation model to data from three countries with divergent epidemiology across Africa, we identify the proportion of cases that need to be serologically tested to achieve target levels of accuracy in estimated infected numbers and discuss how this varies depending on the epidemiological context. Our analysis provides an approach to refining estimates of incidence leveraging all available data, which has the potential to improve allocation of resources, and thus contribute to rapid and efficient control of outbreaks.

Identifying transmission routes of Streptococcus pneumoniae and sources of acquisitions in high transmission communities

Identifying the transmission sources and reservoirs of Streptococcus pneumoniae (SP) is a long-standing question for pneumococcal epidemiology, transmission dynamics, and vaccine policy. Here we use serotype to identify SP transmission and examine acquisitions (in the same household, local community, and county, or of unidentified origin) in a longitudinal cohort of children and adults from the Navajo Nation and the White Mountain Apache American Indian Tribes. We found that adults acquire SP relatively more in the household than other age groups, and children 2-8 years old typically acquire in their own or surrounding communities. Age-specific transmission probability matrices show that transmissions within household were mostly seen from older to younger siblings. Outside the household, children most often transmit to other children in the same age group, showing age-assortative mixing behavior. We find toddlers and older children to be most involved in SP transmission and acquisition, indicating their role as key drivers of SP epidemiology. Although infants have high carriage prevalence, they do not play a central role in transmission of SP compared with toddlers and older children. Our results are relevant to inform alternative pneumococcal conjugate vaccine dosing strategies and analytic efforts to inform optimization of vaccine programs, as well as assessing the transmission dynamics of pathogens transmitted by close contact in general.

Incorporating social contact data in spatio-temporal models for infectious disease spread

Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases-possibly stratified by region and/or age group. We investigate how an age-structured social contact matrix can be incorporated into a spatio-temporal endemic-epidemic model for infectious disease counts. To illustrate the approach, we analyze the spread of norovirus gastroenteritis over six age groups within the 12 districts of Berlin, 2011-2015, using contact data from the POLYMOD study. The proposed age-structured model outperforms alternative scenarios with homogeneous or no mixing between age groups. An extended contact model suggests a power transformation of the survey-based contact matrix toward more within-group transmission.

Measuring the Effect of Soil-Transmitted Helminth Infections on Cognitive Function in Children: Systematic Review and Critical Appraisal of Evidence

Recently the role of soil-transmitted helminth (STH) infections in children's cognitive developmental impairment has been under scrutiny. We conducted a systematic review of the evidence for associations between STH infections and cognitive function of children using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. We aimed to identify the domains of cognitive function in three age strata (<24months, 24-59months and ≥60months) and critically appraise the general design protocol of the studies, with a focus on the cognitive function measurement tools used. A total of 42 papers fulfilled the inclusion criteria, including 10 studies from a recent Cochrane review. Our findings demonstrate variation in tested domains, lack of consistency in the use of measurement tools and analysis of results. Cognitive function measures in children aged under 59months have been mainly limited to domains of gross motor, fine motor and language skills, whereas in children aged 60months and above most studies tested domains such as memory and processing speed. Even within the same age group the results on the association between STH infections and measures of cognitive development were often conflicting. The current study highlights the need for methodological consensus in the use of measurement tools and data analysis protocols if the effect of STH infections on cognitive function domains in children is to be correctly established. This will be an imperative next step to generate conclusive evidence of the role of STH infections in cognitive development in children.

Parametric overdispersed frailty models for current status data

Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. (2010) and Hens et al. (2009). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring.

Examining the quality of childhood tuberculosis diagnosis in Cambodia: a cross-sectional study

BACKGROUND

Cambodia is one of the 22 countries with the highest TB burden. While childhood TB is estimated to make up 10-20% of total TB cases in high-burden settings, this proportion ranges from 1.3 to 39.4% throughout Cambodia's provinces, suggesting potential under- and over-diagnosis of childhood TB, subnationally. The proportion of case notifications classified as extrapulmonary TB out of total TB case notifications in children is 87%, greatly exceeding the expected global range of 20-30%. There is a gap in the literature on how childhood TB is diagnosed in resource-poor settings, and the quality of diagnoses. The study's aim is to quantitatively assess the quality of clinician performance and availability of diagnostic tools, for diagnosing childhood TB in high-burden Operational Districts in Cambodia.

METHODS

Between August and September of 2015, a cross-sectional study was conducted at referral hospitals and villages in 24 high-burden Operational Districts. 40 clinicians, and 104 parents whose child was recently diagnosed with TB were interviewed. Questionnaires assessed availability of diagnostic tools, and clinician knowledge and practice during a clinical examination. Descriptive statistics were calculated to provide cross-sectional data.

RESULTS

Availability of advanced diagnostic tools was low. Only 27.5% of clinicians had Xpert machines available at their facility, and 5% had equipment to perform gastric aspiration. 77.5% of clinicians reported that they had a chest X-ray at their facility, but only 34.6% of parents reported that the clinician conducted a chest X-ray. 72.5% of clinicians could name 5 out of 7 main TB screening criteria; however, parent data suggests that clinicians may not be applying knowledge to practice. The mean number of examinations/tests the clinician conducted during the clinical assessment of the child was 1.64. Of the parents whose child had an enlarged lymph node, 60.22% described lymph node characteristics that were not suggestive of TB.

CONCLUSION

Limited availability of diagnostic tools and suboptimal clinician performance highlight where resources should be allocated to improve quality of diagnoses. Further research needs to be done in low burden Operational Districts to determine the capacity of clinicians and health facilities for diagnosing childhood TB, where cases are likely being missed.

Epidemiology of pertussis in two Ibero-American countries with different vaccination policies: lessons derived from different surveillance systems

Background

Pertussis is a re-emerging disease worldwide despite its high vaccination coverage. European and Latin-American countries have used different surveillance and vaccination policies against pertussis. We compared the epidemiology of this disease in two Ibero-American countries with different vaccination and surveillance policies.

Methods

We compared the epidemiology of pertussis in Spain and the Dominican Republic (DR). We present a 10-year observational study of reported pertussis based on suspected and/or probable cases of pertussis identified by the national mandatory reporting system in both countries between 2005 and 2014. Both countries have a similar case definition for pertussis surveillance, although Spain applies laboratory testing, and uses real time PCR and/or culture for case confirmation while in DR only probable and/or suspected cases are reported. We analyzed incidence, hospitalization, case-fatality rates, mortality and vaccination coverage.

Results

The average annual incidence in children aged <1 year was 3.40/100,000 population in Spain and 12.15/100,000 in DR (p = 0.01). While the incidence in DR was generally higher than in Spain, in 2011 it was six times higher in Spain than in DR. The highest infant mortality in Spain was 0.017/100,000 in 2011, and the highest in DR was 0.08/100,000 in 2014 (p = 0.01). The proportion of hospitalized cases per year among children <1 year varied between 22.0% and 93.7% in Spain, and between 1.1% and 29.4% in DR (p = 0.0002), while mortality varied from 0 to 0.017 and 0 to 0.08 per 100,000 population in Spain and DR, respectively (p = 0.001). Vaccination coverage was 96.5% in Spain and 82.2% in DR (p = 0.001).

Conclusions

Pertussis is a public health problem in both countries. Surveillance, prevention and control measures should be improved, especially in DR. Current vaccination programs are not sufficient for preventing continued pertussis transmission, even in Spain which has high vaccination coverage.

Epidemiology of restaurant-associated foodborne disease outbreaks, United States, 1998-2013

Although contamination of food can occur at any point from farm to table, restaurant food workers are a common source of foodborne illness. We describe the characteristics of restaurant-associated foodborne disease outbreaks and explore the role of food workers by analysing outbreaks associated with restaurants from 1998 to 2013 reported to the Centers for Disease Control and Prevention's Foodborne Disease Outbreak Surveillance System. We identified 9788 restaurant-associated outbreaks. The median annual number of outbreaks was 620 (interquartile range 618-629). In 3072 outbreaks with a single confirmed aetiology reported, norovirus caused the largest number of outbreaks (1425, 46%). Of outbreaks with a single food reported and a confirmed aetiology, fish (254 outbreaks, 34%) was most commonly implicated, and these outbreaks were commonly caused by scombroid toxin (219 outbreaks, 86% of fish outbreaks). Most outbreaks (79%) occurred at sit-down establishments. The most commonly reported contributing factors were those related to food handling and preparation practices in the restaurant (2955 outbreaks, 61%). Food workers contributed to 2415 (25%) outbreaks. Knowledge of the foods, aetiologies, and contributing factors that result in foodborne disease restaurant outbreaks can help guide efforts to prevent foodborne illness.

The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation

Background

We developed the Apollo Structured Vocabulary (Apollo-SV)—an OWL2 ontology of phenomena in infectious disease epidemiology and population biology—as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators.

Results

Apollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures.

In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound.

Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology.

Conclusion

Our ontological analysis as expressed in Apollo-SV was instrumental in developing a simulator-independent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/apollo_sv.owl.

Health and health-related quality of life in pig farmers carrying livestock-associated methicillin-resistant Staphylococcus aureus

There is limited knowledge about the effect of livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) carriage on health-related quality of life (QoL). With this study, we explored whether LA-MRSA causes infections or affects health-related QoL in pig farmers. This prospective cohort study surveyed persons working on 49 farrowing pig farms in The Netherlands for 1 year (2010-2011). On six sampling moments, nasal swabs, environmental samples and questionnaires on activities and infections were collected. At the end of the study year, persons were asked about their QoL using the validated SF-36 and EQ-5D questionnaires. Of 120 persons, 44 (37%) were persistent MRSA carriers. MRSA carriage was not associated with infections, use of antimicrobials, healthcare contact and health-related QoL items in univariate or multivariate analysis, most likely due to the 'healthy worker effect'. Despite high carriage rates, the impact of LA-MRSA carriage in this population of relatively healthy pig farmers on health and health-related QoL appears limited; more research is needed for confirmation.

A case-control study of risk factors for death from 2009 pandemic influenza A(H1N1): is American Indian racial status an independent risk factor?

Historically, American Indian/Alaska Native (AI/AN) populations have suffered excess morbidity and mortality from influenza. We investigated the risk factors for death from 2009 pandemic influenza A(H1N1) in persons residing in five states with substantial AI/AN populations. We conducted a case-control investigation using pandemic influenza fatalities from 2009 in Alaska, Arizona, New Mexico, Oklahoma and Wyoming. Controls were outpatients with influenza. We reviewed medical records and interviewed case proxies and controls. We used multiple imputation to predict missing data and multivariable conditional logistic regression to determine risk factors. We included 145 fatal cases and 236 controls; 22% of cases were AI/AN. Risk factors (P 45 years vs. <18 years], pre-existing medical conditions (mOR 7·1), smoking (mOR 3·0), delayed receipt of antivirals (mOR 6·5), and barriers to healthcare access (mOR 5·3). AI/AN race was not significantly associated with death. The increased influenza mortality in AI/AN individuals was due to factors other than racial status. Prevention of influenza deaths should focus on modifiable factors (smoking, early antiviral use, access to care) and identifying high-risk persons for immunization and prompt medical attention.

High burden of invasive group A streptococcal disease in the Northern Territory of Australia

Although the incidence of invasive group A streptococcal disease in northern Australia is very high, little is known of the regional epidemiology and molecular characteristics. We conducted a case series of Northern Territory residents reported between 2011 and 2013 with Streptococcus pyogenes isolates from a normally sterile site. Of the 128 reported episodes, the incidence was disproportionately high in the Indigenous population at 69·7/100 000 compared to 8·8/100 000 in the non-Indigenous population. Novel to the Northern Territory is the extremely high incidence in haemodialysis patients of 2205·9/100 000 population; and for whom targeted infection control measures could prevent transmission. The incidences in the tropical north and semi-arid Central Australian regions were similar. Case fatality was 8% (10/128) and streptococcal toxic shock syndrome occurred in 14 (11%) episodes. Molecular typing of 82 isolates identified 28 emm types, of which 63 (77%) were represented by four emm clusters. Typing confirmed transmission between infant twins. While the diverse range of emm types presents a challenge for effective coverage by vaccine formulations, the limited number of emm clusters raises optimism should cluster-specific cross-protection prove efficacious. Further studies are required to determine effectiveness of chemoprophylaxis for contacts and to inform public health response.

Characterization of the live salmonid movement network in Ireland: Implications for disease prevention and control

Live fish movement is considered as having an important role in the transmission of infectious diseases. For that reason, interventions for cost-effective disease prevention and control rely on a sound understanding of the patterns of live fish movements in a region or country. Here, we characterize the network of live fish movements in the Irish salmonid farming industry during 2013, using social network analysis and spatial epidemiology methods, and identify interventions to limit the risk of disease introduction and spread. In the network there were 62 sites sending and/or receiving fish, with a total of 130 shipments (84 arcs) comprising approx. 17.2 million fish during the year. Atlantic salmon shipments covered longer distances than trout shipments, with some traversing the entire country. The average shipment of Atlantic salmon was 146,186 (SD 194,344) fish, compared to 77,928 (127,009) for trout, however, variability was high. There were 3 periods where shipments peaked (February-April, June-September, and November), which were related to specific stages of fish. The network was disconnected and had two major weak components, the first one with 39 nodes (mostly Atlantic salmon sites), and the second one with 10 nodes (exclusively trout sites). Correlation between in and out-degree at each site and assortativity coefficient were slightly low and non-significant: -0.08 (95% CI: -0.22, 0.06) and -0.13 (95% CI: -0.36, 0.08), respectively, indicating random mixing with regard to node degree. Although competing models also produced a good fit to degree distribution, it is likely that the network possesses both small-world and scale-free topology. This would facilitate the spread and persistence of infection in the salmon production system, but would also facilitate the design of risk-based surveillance strategies by targeting hubs, bridges or cut-points. Using Infomap community detection algorithms, 2 major communities were identified within the giant weak component, which were linked by only 4 nodes. Communities found had no correspondence with geographical zones within the country, which could potentially hinder the implementation of zoning strategies for disease control and eradication. Three significant spatial clusters of node centrality measures were detected, two in county Donegal (betweenness and outcloseness) and one in county Galway (incloseness), highlighting the importance of these locations as hot spots of highly central sites with a higher potential for both introduction and spread of infection. These results will assist in the design and implementation of measures to reduce the sanitary risks emerging from live fish trade within Ireland.

The role of the intensive care unit in real-time surveillance of emerging pandemics: the Italian GiViTI experience

The prompt availability of reliable epidemiological information on emerging pandemics is crucial for public health policy-makers. Early in 2013, a possible new H1N1 epidemic notified by an intensive care unit (ICU) to GiViTI, the Italian ICU network, prompted the re-activation of the real-time monitoring system developed during the 2009-2010 pandemic. Based on data from 216 ICUs, we were able to detect and monitor an outbreak of severe H1N1 infection, and to compare the situation with previous years. The timely and correct assessment of the severity of an epidemic can be obtained by investigating ICU admissions, especially when historical comparisons can be made.

A clonal outbreak of tuberculosis in a homeless population in the interior of British Columbia, Canada, 2008-2015

A tuberculosis (TB) case was reported May 2008 in Kelowna, British Columbia, leading to a multi-year outbreak in homeless persons. The epidemiological characteristics and social networks of cases are described. Outbreak-related cases were identified from epidemiological information in medical records and from genotyping of TB isolates. Social network information from case interviews were used to identify potential locations of TB transmission, where symptom screening and tuberculin skin testing was conducted. Fifty-two cases that were predominantly male (47/52), Canadian-born (44/50), and were homeless or associated with homeless individuals (42/52) were reported from May 2008 to May 2014. Many isolates (40/49) had partial resistance to isoniazid. Transmission primarily occurred at two homeless shelters, with potential further transmission at sites visited by the general population. TB outbreaks in homeless populations can occur in small, low-incidence cities. Social network information helped prioritize sites for TB screening, thereby improving detection of persons with TB disease or latent infection for treatment.

Staphylococcus aureus infections in New Zealand, 2000-2011

Skin and soft tissue infections increased significantly; sociodemographic disparity was noted.

The incidence rate for invasive and noninvasive Staphylococcus aureus infections in New Zealand is among the highest reported in the developed world. Using nationally collated hospital discharge data, we analyzed the epidemiology of serious S. aureus infections in New Zealand during 2000–2011. During this period, incidence of S. aureus skin and soft tissue infections increased significantly while incidence of staphylococcal sepsis and pneumonia remained stable. We observed marked ethnic and sociodemographic inequality across all S. aureus infections; incidence rates for all forms of S. aureus infections were highest among Māori and Pacific Peoples and among patients residing in areas of high socioeconomic deprivation. The increased incidence of S. aureus skin and soft tissue infections, coupled with the demographic disparities, is of considerable concern. Future work should aim to reduce this disturbing national trend.

Acute gastrointestinal illness following a prolonged community-wide water emergency

The drinking water infrastructure in the United States is ageing; extreme weather events place additional stress on water systems that can lead to interruptions in the delivery of safe drinking water. We investigated the association between household exposures to water service problems and acute gastrointestinal illness (AGI) and acute respiratory illness (ARI) in Alabama communities that experienced a freeze-related community-wide water emergency. Following the water emergency, investigators conducted a household survey. Logistic regression models were used to estimate adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) for self-reported AGI and ARI by water exposures. AGI was higher in households that lost water service for ⩾7 days (aPR 2·4, 95% CI 1·1-5·2) and experienced low water pressure for ⩾7 days (aPR 3·6, 95% CI 1·4-9·0) compared to households that experienced normal service and pressure; prevalence of AGI increased with increasing duration of water service interruptions. Investments in the ageing drinking water infrastructure are needed to prevent future low-pressure events and to maintain uninterrupted access to the fundamental public health protection provided by safe water supplies. Households and communities need to increase their awareness of and preparedness for water emergencies to mitigate adverse health impacts.

Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011

A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.

Evidence that pneumococcal serotype replacement in Massachusetts following conjugate vaccination is now complete

Invasive pneumococcal disease (IPD) has been reduced in the US following conjugate vaccination (PCV7) targeting seven pneumococcal serotypes in 2000. However, increases in IPD due to other serotypes have been observed, in particular 19A. How much this "serotype replacement" will erode the benefits of vaccination and over what timescale is unknown. We used a population genetic approach to test first whether the selective impact of vaccination could be detected in a longitudinal carriage sample, and secondly how long it persisted for following introduction of vaccine in 2000. To detect the selective impact of the vaccine we compared the serotype diversity of samples from pneumococcal carriage in Massachusetts children collected in 2001, 2004 and 2007 with others collected in the pre-vaccine era in Massachusetts, the UK and Finland. The 2004 sample was significantly (p >0.0001) more diverse than pre-vaccine samples, indicating the selective pressure of vaccination. The 2007 sample showed no significant difference in diversity from the pre-vaccine period, and exhibited similar population structure, but with different serotypes. In 2007 the carriage frequency of 19A was similar to that of the most common serotype in pre-vaccine samples. We suggest that serotype replacement involving 19A may be complete in Massachusetts due to similarities in population structure to pre-vaccine samples. These results suggest that the replacement phenomenon occurs rapidly with high vaccine coverage, and may allay concerns about future increases in disease due to 19A. For other serotypes, the future course of replacement disease remains to be determined.