Sheep scab transmission: a spatially explicit dynamic metapopulation model

Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain. The model uses agricultural survey and sheep movement data to geo-reference farms and capture realistic movement patterns. Reported data on sheep scab outbreaks from 1973 to 1991 were used for model fitting with Sequential Monte Carlo Approximate Bayesian Computation methods. The outbreak incidence predicted by the model was from the same statistical distribution as the reported outbreak data (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\chi }^{2}$$\end{document}χ2 = 115.3, p = 1) and the spatial location of sheep scab outbreaks predicted was positively correlated with the observed outbreak data by county (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ = 0.55, p < 0.001), confirming that the model developed is able to accurately capture the number of farms infected in a year, the seasonality of scab incidence and the spatial patterns seen in the data. This model gives insight into the transmission dynamics of sheep scab and will allow the exploration of more effective control strategies.

Prevalence, genomic characteristics, and transmission dynamics of mcr-1-positive Salmonella enterica Typhimurium from patients with infectious diarrhea

BACKGROUND

Previous studies reported the prevalence of mcr-1 among clinical infected Salmonella isolates in China. However, the transmission dynamics of mcr-1 in different ecological niches were not well investigated. Our objective is to exhibit the transmission dynamics of mcr-1 in Salmonella.

METHODS

598 Salmonella isolates were recovered from ten hospitals; besides 936 pig faces and 167 pork samples were collected from January 2015 to December 2017 in Guangzhou, China. PCR and sequencing were used to identify mcr-1-positive Salmonella. Antimicrobial susceptibility testing was performed with 16 antimicrobials. Conjugation, S1-PFGE, and Southern blot were used to determine the transferability and location of mcr-1. Whole-genome sequencing was used to investigate pangenome, phylogeny, plasmid, and transposon.

RESULTS

Eleven mcr-1-positive Salmonella isolates were identified from patients with infectious diarrhea. Five pig fecal samples and three pork samples contained mcr-1-positive Salmonella isolates. All isolates were multi-drug resistant. The mcr-1 genes were located on ∼210-250 kb IncHI2-pST3 plasmids, and 12 mcr-1 genes were transferable. All isolates were assigned to ST34 or its genetically closed STs. The distribution of the core-genome network was significantly correlated with source distributions. The accessory genes-based network demonstrated that the diverse clonal complexes could share highly similar accessory genomes.

CONCLUSIONS

The prevalence of mcr-1-positive Salmonella among different sources was low. Clonal transmission could not be the main reason for the expansion of mcr-1-positive Salmonella, but be attributed to the horizontal transfer of IncHI2-pST3 plasmid. Continuous surveillance on Salmonella should be performed to investigate the response of colistin banning in food-producing animals by mcr-1-positive Salmonella populations.

Coupled dynamics of behaviour and disease contagion among antagonistic groups

Disease transmission and behaviour change are both fundamentally social phenomena. Behaviour change can have profound consequences for disease transmission, and epidemic conditions can favour the more rapid adoption of behavioural innovations. We analyse a simple model of coupled behaviour change and infection in a structured population characterised by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioural divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behaviour-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a 'second wave' in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarised populations such as the USA.

Proactive and blended approach for COVID-19 control in Taiwan

Coronavirus disease 2019 (COVID-19) has become the greatest threat to human society in a century. To better devise control strategies, policymakers should adjust policies based on scientific evidence in hand. Several countries have limited the epidemics of COVID-19 by prioritizing containment strategies to mitigate the impacts on public health and healthcare systems. However, asymptomatic/pre-symptomatic transmission of COVID-19 complicated traditional symptom-based approaches for disease control. In addition, drastic population-based interventions usually have significant societal and economic impacts. Therefore, in Taiwan, the containment strategies consisted of the more extended case-based interventions (e.g., case detection with enhanced surveillance and contact tracing with active monitoring and quarantine of close contacts) and more targeted population-based interventions (e.g., face mask use in recommended settings and risk-oriented border control with corresponding quarantine requirement). The success of the blended approach emphasizes not only the importance of evidence-supported policymaking but also the coordinated efforts between the government and the people.

Characteristics of SARS-CoV-2 positive cases beyond health-care professionals or social and health-care facilities

Background

During the outbreak of SARS-CoV-2 in Italy, infection among health-care professionals and in the context of welfare and health-care facilities was a significant concern. It is known that the elderly or those with concomitant pathologies are at greater risk of a serious evolution of the disease if affected by COVID-19 and that health workers are a category with greater exposure to SARS-CoV-2 infection. Until now, there has been little information on the epidemiological features and transmission dynamics of the COVID-19 outbreak which did not involve health-care professionals or social and health-care facilities. For this reason, this paper aims to describe the epidemiological characteristics of SARS-CoV-2 infection in the general population outside these semi-closed communities.

Methods

The study was designed by analyzing the data of the 1371 SARS-CoV-2 positive subjects observed in Sardinia up to 9 July, 2020 and whose data were available in the public health department. Statistical analysis and graphic representation were performed using STATA and Adobe Illustrator, respectively.

Results

Of the positive cases analyzed, 323 (23.5%) are health-care workers and 563 (41.1%) reside in social or health-care facilities. The number of positive cases among the general population (subjects who do not belong to these semi-closed communities), is 399 (29.1%), 208 females and 191 males. The estimated Case Fatality Rate stands at 5.0%, which is almost half the rate reported for all the SARS-CoV-2 positive cases (9.8%). The geographical distribution of positive cases differs considerably from the distribution of the totality of cases in Sardinia.

Conclusions

This review provides an insight into the COVID-19 situation in the general community, ie not involving health-care professionals or social and health-care facilities. Understanding the evolving epidemiology and transmission dynamics of the outbreak outside of these semi-closed communities would provide appropriate information to guide intervention policy. The COVID-19 pandemic has exacerbated the vulnerability of our health-care system. Severe disruptions in care, medicine shortages and unequal access to health-care are but a few examples of the challenges faced by people living in Italy and Europe, highlighting the importance of evidence-based approaches in supporting the development of prevention and response strategies for future pandemics.

Characteristics of SARS-CoV-2 positive cases beyond health-care professionals or social and health-care facilities

BACKGROUND

During the outbreak of SARS-CoV-2 in Italy, infection among health-care professionals and in the context of welfare and health-care facilities was a significant concern. It is known that the elderly or those with concomitant pathologies are at greater risk of a serious evolution of the disease if affected by COVID-19 and that health workers are a category with greater exposure to SARS-CoV-2 infection. Until now, there has been little information on the epidemiological features and transmission dynamics of the COVID-19 outbreak which did not involve health-care professionals or social and health-care facilities. For this reason, this paper aims to describe the epidemiological characteristics of SARS-CoV-2 infection in the general population outside these semi-closed communities.

METHODS

The study was designed by analyzing the data of the 1371 SARS-CoV-2 positive subjects observed in Sardinia up to 9 July, 2020 and whose data were available in the public health department. Statistical analysis and graphic representation were performed using STATA and Adobe Illustrator, respectively.

RESULTS

Of the positive cases analyzed, 323 (23.5%) are health-care workers and 563 (41.1%) reside in social or health-care facilities. The number of positive cases among the general population (subjects who do not belong to these semi-closed communities), is 399 (29.1%), 208 females and 191 males. The estimated Case Fatality Rate stands at 5.0%, which is almost half the rate reported for all the SARS-CoV-2 positive cases (9.8%). The geographical distribution of positive cases differs considerably from the distribution of the totality of cases in Sardinia.

CONCLUSIONS

This review provides an insight into the COVID-19 situation in the general community, ie not involving health-care professionals or social and health-care facilities. Understanding the evolving epidemiology and transmission dynamics of the outbreak outside of these semi-closed communities would provide appropriate information to guide intervention policy. The COVID-19 pandemic has exacerbated the vulnerability of our health-care system. Severe disruptions in care, medicine shortages and unequal access to health-care are but a few examples of the challenges faced by people living in Italy and Europe, highlighting the importance of evidence-based approaches in supporting the development of prevention and response strategies for future pandemics.

Transmission dynamics and control methodology of COVID-19: A modeling study

The coronavirus disease 2019 (COVID-19) has grown up to be a pandemic within a short span of time. To investigate transmission dynamics and then determine control methodology, we took epidemic in Wuhan as a study case. Unfortunately, to our best knowledge, the existing models are based on the common assumption that the total population follows a homogeneous spatial distribution, which is not the case for the prevalence occurred both in the community and in hospital due to the difference in the contact rate. To solve this problem, we propose a novel epidemic model called SEIR-HC, which is a model with two different social circles (i.e., individuals in hospital and community). Using the model alongside the exclusive optimization algorithm, the spread process of COVID-19 epidemic in Wuhan city is reproduced and then the propagation characteristics and unknown data are estimated. The basic reproduction number of COVID-19 is estimated to be 7.9, which is far higher than that of the severe acute respiratory syndrome (SARS). Furthermore, the control measures implemented in Wuhan are assessed and the control methodology of COVID-19 is discussed to provide guidance for limiting the epidemic spread.

Transmission dynamics and control methodology of COVID-19: A modeling study

The coronavirus disease 2019 (COVID-19) has grown up to be a pandemic within a short span of time. To investigate transmission dynamics and then determine control methodology, we took epidemic in Wuhan as a study case. Unfortunately, to our best knowledge, the existing models are based on the common assumption that the total population follows a homogeneous spatial distribution, which is not the case for the prevalence occurred both in the community and in hospital due to the difference in the contact rate. To solve this problem, we propose a novel epidemic model called SEIR-HC, which is a model with two different social circles (i.e., individuals in hospital and community). Using the model alongside the exclusive optimization algorithm, the spread process of COVID-19 epidemic in Wuhan city is reproduced and then the propagation characteristics and unknown data are estimated. The basic reproduction number of COVID-19 is estimated to be 7.9, which is far higher than that of the severe acute respiratory syndrome (SARS). Furthermore, the control measures implemented in Wuhan are assessed and the control methodology of COVID-19 is discussed to provide guidance for limiting the epidemic spread.

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources.

METHODS

We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing.

RESULTS

In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections.

CONCLUSIONS

COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19

The SARS-CoV-2 driven disease COVID-19 is pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. To launch both containment and mitigation measures, each country requires estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and to design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory of COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India. We show that our approach predicts state-wise COVID-19 spread for each country with reasonable accuracy. We show that Rt, as the effective reproduction number, exhibits significant spatial variations in these countries. However, by accounting for the spatial variation of Rt in an adaptive fashion, the predictive model provides estimates of the possible asymptomatic and undetected COVID-19 cases, both of which are key contributors in COVID-19 transmission. We have applied our methodology to make detailed predictions for COVID19 incidences at the district and state level in India. Finally, to make the models available to the public at large, we have developed a web-based dashboard, namely "Predictions and Assessment of Corona Infections and Transmission in India" (PRACRITI, see http://pracriti.iitd.ac.in), which provides the detailed Rt values and a three-week forecast of COVID cases.

Effect of the early use of antivirals on the COVID-19 pandemic. A computational network modeling approach

It seems that we are far from controlling COVID-19 pandemics, and, consequently, returning to a fully normal life. Until an effective vaccine is found, safety measures as the use of face masks, social distancing, washing hands regularly, etc., have to be taken. Also, the use of appropriate antivirals in order to alleviate the symptoms, to control the severity of the illness and to prevent the transmission, could be a good option that we study in this work. In this paper, we propose a computational random network model to study the transmission dynamics of COVID-19 in Spain. Once the model has been calibrated and validated, we use it to simulate several scenarios where effective antivirals are available. The results show how the early use of antivirals may significantly reduce the incidence of COVID-19 and may avoid a new collapse of the health system.

A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we reveal that pre-symptomatic transmission accelerates the onset of the exponential growth of cases. After that, we demonstrate that the persistence of SARS-CoV-2 on hard surfaces determines the number of cases reached during the peak of the epidemic. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China.

High Plasmodium infection intensity in naturally infected malaria vectors in Africa

The population dynamics of human to mosquito malaria transmission in the field has important implications for the genetics, epidemiology and control of malaria. The number of oocysts in oocyst-positive mosquitoes developing from a single, naturally acquired infectious blood meal (herein referred to as a single-feed infection load) greatly influences the efficacy of transmission blocking interventions but still remains poorly documented. During a year-long analysis of malaria parasite transmission in Burkina Faso we caught and dissected wild malaria vectors to assess Plasmodium oocyst prevalence and load (the number of oocysts counted in mosquitoes with detectable oocysts) and the prevalence of salivary gland sporozoites. This was compared with malaria endemicity in the human population, assessed in cross-sectional surveys. Data were analysed using a novel transmission mathematical model to estimate the per bite transmission probability and the average single-feed infection load for each location. The observed oocyst load and the estimated single-feed infection load in naturally infected mosquitoes were substantially higher than previous estimates (means ranging from 3.2 to 24.5 according to seasons and locations) and indicate a strong positive association between the single-feed infection load and parasite prevalence in humans. This work suggests that highly infected mosquitoes are not rare in the field and might have a greater influence on the epidemiology and genetics of the parasite, and on the efficacy of novel transmission blocking interventions.

Similarity maps and pairwise predictions for transmission dynamics of COVID-19 with neural networks

On March 11, 2020, the World Health Organization declared COVID-19 as a pandemic. Since then, many countries have experienced the rapid transmission of this respiratory disease among their populations and have exercised many strategies to mitigate the spread of this disease. The prediction of the transmission dynamics serves important roles in designing mitigation strategies. However, due to the unknown characteristics of this disease, as well as the geographical and political factors, building efficient models of the dynamics for many countries is difficult. The objective of this study is to develop a transmission dynamics predictor that takes advantage of the time differences among many countries with respect to transmission of this disease, in that some countries experienced earlier outbreaks than others. The primary novelty of the proposed method is that, unlike many existing transmission predictors that require parameters based on prior knowledge of the epidemiology of past viruses, the proposed method only requires the transmission similarities between countries in the publicly available data for this current disease. In this paper, the viability and limitations of the proposed method are reported and discussed.

Transmission patterns of COVID-19 in the mainland of China and the efficacy of different control strategies: a data- and model-driven study

BACKGROUND

The coronavirus disease 2019 (COVID-19) outbreak has seriously endangered the health and lives of Chinese people. In this study, we predicted the COVID-19 epidemic trend and estimated the efficacy of several intervention strategies in the mainland of China.

METHODS

According to the COVID-19 epidemic status, we constructed a compartmental model. Based on reported data from the National Health Commission of People's Republic of China during January 10-February 17, 2020, we estimated the model parameters. We then predicted the epidemic trend and transmission risk of COVID-19. Using a sensitivity analysis method, we estimated the efficacy of several intervention strategies.

RESULTS

The cumulative number of confirmed cases in the mainland of China will be 86 763 (95% CI: 86 067-87 460) on May 2, 2020. Up until March 15, 2020, the case fatality rate increased to 6.42% (95% CI: 6.16-6.68%). On February 23, 2020, the existing confirmed cases reached its peak, with 60 890 cases (95% CI: 60 350-61 431). On January 23, 2020, the effective reproduction number was 2.620 (95% CI: 2.567-2.676) and had dropped below 1.0 since February 5, 2020. Due to governmental intervention, the total number of confirmed cases was reduced by 99.85% on May 2, 2020. Had the isolation been relaxed from February 24, 2020, there might have been a second peak of infection. However, relaxing the isolation after March 16, 2020 greatly reduced the number of existing confirmed cases and deaths. The total number of confirmed cases and deaths would increase by 8.72 and 9.44%, respectively, due to a 1-day delayed diagnosis in non-isolated infected patients. Moreover, if the coverage of close contact tracing was increased to 100%, the cumulative number of confirmed cases would be decreased by 88.26% on May 2, 2020.

CONCLUSIONS

The quarantine measures adopted by the Chinese government since January 23, 2020 were necessary and effective. Postponing the relaxation of isolation, early diagnosis, patient isolation, broad close-contact tracing, and strict monitoring of infected persons could effectively control the COVID-19 epidemic. April 1, 2020 would be a reasonable date to lift quarantine in Hubei and Wuhan.

SOCRATES: an online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

Objective

Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.

Results

We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{0}$$\end{document}0. We incorporated location-specific physical distancing measures (e.g. school closure or at work) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that physical distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

SOCRATES: an online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

OBJECTIVE

Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.

RESULTS

We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R[Formula: see text]. We incorporated location-specific physical distancing measures (e.g. school closure or at work) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that physical distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

Understanding Transmission Dynamics of COVID-19-Type Infections by Direct Numerical Simulations of Cough/Sneeze Flows

The transmission dynamics of highly contagious respiratory diseases like COVID-19 (through coughing/sneezing) is an open problem in the epidemiological studies of such diseases (Bourouiba, JAMA. https://doi.org/10.1001/jama.2020.4756. 2020). The problem is basically the fluid dynamics of a transient turbulent jet/puff with buoyancy, laden with evaporating droplets carrying the pathogen. A turbulent flow of this nature does not lend itself to reliable estimates through modeling approaches such as RANS (Reynolds-Averaged Navier-Stokes equations) or other droplet-based models. However, direct numerical simulations (DNS) of what may be called "cough/sneeze flows" can play an important role in understanding the spread of the contagion. The objective of this work is to develop a DNS code for studying cough/sneeze flows by a suitable combination of the DNS codes available with the authors (developed to study cumulus cloud flows including thermodynamics of phase change and the dynamics of small water droplets) and to generate useful data on these flows. Recent results from the cumulus cloud simulations are included to highlight the effect of turbulent entrainment (which is one of the key processes in determining the spread of the expiratory flows) on the distribution of liquid water content in a moist plume. Furthermore, preliminary results on the temperature distribution in a "dry cough" (i.e., without inclusion of liquid droplets) are reported to illustrate the large spatial extent and time duration over which the cough flow can persist after the coughing has stopped. We believe that simulations of this kind can help to devise more accurate guidelines for separation distances between neighbors in a group, design better masks, and minimize the spread of respiratory diseases of the COVID-19 type.

Contrasting animal movement and spatial connectivity networks in shaping transmission pathways of a genetically diverse virus

Analyses of livestock movement networks has become key to understanding an industry's vulnerability to infectious disease spread and for identifying farms that play disproportionate roles in pathogen dissemination. In addition to animal movements, many pathogens can spread between farms via mechanisms mediated by spatial proximity. Heterogeneities in contact patterns based on spatial proximity are less commonly considered in network studies, and studies that jointly consider spatial connectivity and animal movement are rare. The objective of this study was to determine the extent to which movement versus spatial proximity networks determine the distribution of an economically important endemic virus, porcine reproductive and respiratory syndrome virus (PRRSV), within a swine-dense region of the U.S. PRRSV can be classified into numerous phylogenetic lineages. Such data can be used to better resolve between-farm infection chains and elucidate types of contact most associated with transmission. Here, we construct movement and spatial proximity networks; farms within the networks were classified as cases if a given PRRSV lineage had been recovered at least once in a year for each of three years analyzed. We evaluated six lineages and sub-lineages across three years, and evaluated the epidemiological relevance of each network by applying network k-tests to statistically evaluate whether the pattern of case occurrence within the network was consistent with transmission via network linkages. Our results indicated that animal movements, not local area spread, play a dominant role in shaping transmission pathways, though there were differences amongst lineages. The median number of case farms inter-linked via animal movements was approximately 4.1x higher than random expectations (range: 1.7-13.7; p < 0.05, network k-test), whereas this measure was only 2.7x higher than random expectations for farms linked via spatial proximity (range: 1.3-5.4; p < 0.05, network k-test). For spatial proximity networks, contact based on proximities of <5 km appeared to have greater epidemiological relevance than longer distances, likely related to diminishing probabilities of local area spread at greater distances. However, the greater overall levels of connectivity of the spatial network compared to the movement network highlights the vulnerability of pig populations to widespread transmission via this route. By combining genetic data with network analysis, this research advances our understanding of dynamics of between-farm spread of PRRSV, helps establish the relative importance of transmission via animal movements versus local area spread, and highlights the potential for targeted control strategies based upon heterogeneities in network connectivity.

Effect of rodent density on tick and tick-borne pathogen populations: consequences for infectious disease risk

BACKGROUND

Rodents are considered to contribute strongly to the risk of tick-borne diseases by feeding Ixodes ricinus larvae and by acting as amplifying hosts for pathogens. Here, we tested to what extent these two processes depend on rodent density, and for which pathogen species rodents synergistically contribute to the local disease risk, i.e. the density of infected nymphs (DIN).

METHODS

In a natural woodland, we manipulated rodent densities in plots of 2500 m² by either supplementing a critical food source (acorns) or by removing rodents during two years. Untreated plots were used as controls. Collected nymphs and rodent ear biopsies were tested for the presence of seven tick-borne microorganisms. Linear models were used to capture associations between rodents, nymphs, and pathogens.

RESULTS

Investigation of data from all plots, irrespective of the treatment, revealed a strong positive association between rodent density and nymphal density, nymphal infection prevalence (NIP) with Borrelia afzelii and Neoehrlichia mikurensis, and hence DIN's of these pathogens in the following year. The NIP, but not the DIN, of the bird-associated Borrelia garinii, decreased with increasing rodent density. The NIPs of Borrelia miyamotoi and Rickettsia helvetica were independent of rodent density, and increasing rodent density moderately increased the DINs. In addition, NIPs of Babesia microti and Spiroplasma ixodetis decreased with increasing rodent density, which had a non-linear association with DINs of these microorganisms.

CONCLUSIONS

A positive density dependence for all rodent- and tick-associated tick-borne pathogens was found, despite the observation that some of them decreased in prevalence. The effects on the DINs were variable among microorganisms, more than likely due to contrasts in their biology (including transmission modes, host specificity and transmission efficiency). The strongest associations were found in rodent-associated pathogens that most heavily rely on horizontal transmission. Our results draw attention to the importance of considering transmission mode of a pathogen while developing preventative measures to successfully reduce the burden of disease.

Transmission dynamics of COVID-19 in Wuhan, China: effects of lockdown and medical resources

Due to the strong infectivity of COVID-19, it spread all over the world in about three months and thus has been studied from different aspects including its source of infection, pathological characteristics, diagnostic technology and treatment. Yet, the influences of control strategies on the transmission dynamics of COVID-19 are far from being well understood. In order to reveal the mechanisms of disease spread, we present dynamical models to show the propagation of COVID-19 in Wuhan. Based on mathematical analysis and data analysis, we systematically explore the effects of lockdown and medical resources on the COVID-19 transmission in Wuhan. It is found that the later lockdown is adopted by Wuhan, the fewer people will be infected in Wuhan, and nevertheless it will have an impact on other cities in China and even the world. Moreover, the richer the medical resources, the higher the peak of new infection, but the smaller the final scale. These findings well indicate that the control measures taken by the Chinese government are correct and timely.

Impact of vertebrate communities on Ixodes ricinus-borne disease risk in forest areas

Background

The density of questing ticks infected with tick-borne pathogens is an important parameter that determines tick-borne disease risk. An important factor determining this density is the availability of different wildlife species as hosts for ticks and their pathogens. Here, we investigated how wildlife communities contribute to tick-borne disease risk. The density of Ixodes ricinus nymphs infected with Borrelia burgdorferi (sensu lato), Borrelia miyamotoi, Neoehrlichia mikurensis and Anaplasma phagocytophilum among 19 forest sites were correlated to the encounter probability of different vertebrate hosts, determined by encounter rates as measured by (camera) trapping and mathematical modeling.

Result

We found that the density of any tick life stage was proportional to the encounter probability of ungulates. Moreover, the density of nymphs decreased with the encounter probability of hare, rabbit and red fox. The density of nymphs infected with the transovarially-transmitted B. miyamotoi increased with the density of questing nymphs and the encounter probability of bank vole. The density of nymphs infected with all other pathogens increased with the encounter probability of competent hosts: bank vole for Borrelia afzelii and N. mikurensis, ungulates for A. phagocytophilum and blackbird for Borrelia garinii and Borrelia valaisiana. The negative relationship we found was a decrease in the density of nymphs infected with B. garinii and B. valaisiana with the encounter probability of wood mouse.

Conclusions

Only a few animal species drive the densities of infected nymphs in forested areas. There, foxes and leporids have negative effects on tick abundance, and consequently on the density of infected nymphs. The abundance of competent hosts generally drives the abundances of their tick-borne pathogen. A dilution effect was only observed for bird-associated Lyme spirochetes.

Direct transmission of within-host Mycobacterium tuberculosis diversity to secondary cases can lead to variable between-host heterogeneity without de novo mutation: A genomic investigation

BACKGROUND

Whole genome sequencing (WGS) has enabled the development of new approaches to track Mycobacterium tuberculosis (Mtb) transmission between tuberculosis (TB) cases but its utility may be challenged by the discovery that Mtb diversifies within hosts. Nevertheless, there is limited data on the presence and degree of within-host evolution.

METHODS

We profiled a well-documented Mtb transmission cluster with three pulmonary TB cases to investigate within-host evolution and describe its impact on recent transmission estimates. We used deep sequencing to track minority allele frequencies (<50·0% abundance) during transmission and standard treatment.

FINDINGS

Pre-treatment (n = 3) and serial samples collected over 2 months of antibiotic treatment (n = 16) from all three cases were analysed. Consistent with the epidemiological data, zero fixed SNP separated all genomes. However, we identified six subclones between the three cases with an allele frequency ranging from 35·0% to 100·0% across sampling intervals. Five subclones were identified within the index case pre-treatment and shared with one secondary case, while only the dominant clone was observed in the other secondary case. By tracking the frequency of these heterogeneous alleles over the two-month therapy, we observed distinct signatures of drift and negative selection, but limited evidence for de novo mutations, even under drug pressure.

INTERPRETATION

We document within-host Mtb diversity in an index case, which led to transmission of minority alleles to a secondary case. Incorporating data on heterogeneous alleles may refine our understanding of Mtb transmission dynamics. However, more evidence is needed on the role of transmission bottleneck on observed heterogeneity between cases.

Anaplasma phagocytophilum evolves in geographical and biotic niches of vertebrates and ticks

Background

Anaplasma phagocytophilum is currently regarded as a single species. However, molecular studies indicate that it can be subdivided into ecotypes, each with distinct but overlapping transmission cycle. Here, we evaluate the interactions between and within clusters of haplotypes of the bacterium isolated from vertebrates and ticks, using phylogenetic and network-based methods.

Methods

The presence of A. phagocytophilum DNA was determined in ticks and vertebrate tissue samples. A fragment of the groEl gene was amplified and sequenced from qPCR-positive lysates. Additional groEl sequences from ticks and vertebrate reservoirs were obtained from GenBank and through literature searches, resulting in a dataset consisting of 1623 A. phagocytophilum field isolates. Phylogenetic analyses were used to infer clusters of haplotypes and to assess phylogenetic clustering of A. phagocytophilum in vertebrates or ticks. Network-based methods were used to resolve host-vector interactions and their relative importance in the segregating communities of haplotypes.

Results

Phylogenetic analyses resulted in 199 haplotypes within eight network-derived clusters, which were allocated to four ecotypes. The interactions of haplotypes between ticks, vertebrates and geographical origin, were visualized and quantified from networks. A high number of haplotypes were recorded in the tick Ixodes ricinus. Communities of A. phagocytophilum recorded from Korea, Japan, Far Eastern Russia, as well as those associated with rodents had no links with the larger set of isolates associated with I. ricinus, suggesting different evolutionary pressures. Rodents appeared to have a range of haplotypes associated with either Ixodes trianguliceps or Ixodes persulcatus and Ixodes pavlovskyi. Haplotypes found in rodents in Russia had low similarities with those recorded in rodents in other regions and shaped separate communities.

Conclusions

The groEl gene fragment of A. phagocytophilum provides information about spatial segregation and associations of haplotypes to particular vector-host interactions. Further research is needed to understand the circulation of this bacterium in the gap between Europe and Asia before the overview of the speciation features of this bacterium is complete. Environmental traits may also play a role in the evolution of A. phagocytophilum in ecotypes through yet unknown relationships.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3583-8) contains supplementary material, which is available to authorized users.

Molecular epidemiology of Clostridioides difficile in domestic dogs and zoo animals

Animals such as domestic dogs and zoo animals reside in close proximity to humans and could contribute to the dissemination of Clostridioides difficile spores which are common in the community environment. The purpose of this study was to assess C. difficile colonization in domestic dogs attending a day boarding facility and zoo animals receiving systemic antibiotics. Stool samples and paw swabs were collected from dogs who attended a day boarding facility. Stool samples were also collected from zoo animals starting systemic antibiotics. Finally, environmental samples were collected from nearby public parks. Stool samples and swabs were incubated anaerobically in enrichment broth for C. difficile growth, PCR was done to confirm presence of toxin genes, and PCR ribotyping was performed for strain characterization. During the study period, 136 dog stool samples were obtained, the paws of 16 dogs were swabbed, and 250 environmental swabs from surrounding public parks were obtained. Twenty-three of 136 dog stool samples (17%) and 9 of 16 dog paws sampled (56%) grew toxigenic C. difficile. One hundred and four stool samples from 49 zoo animals were collected of which 19 (18%) grew toxigenic C. difficile. Rates of toxigenic C. difficile colonization increased significantly during antibiotic therapy (33%) and then returned to baseline during the follow-up (11%) period (p = 0.019). Fifty-five of 250 environmental swabs from public parks (22%) grew toxigenic C. difficile. Ribotypes associated with human disease including 106 and 014-020 were isolated from all sources. This study demonstrated a high rate of toxigenic C. difficile colonization in domestic dogs and zoo animals with ribotypes similar to those causing human disease. These results demonstrate the relationship between humans, animals, and the environment in the dissemination of spores.

Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil

BACKGROUND

The parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease. Leishmania infantum parasites are transmitted between hosts during blood-feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models.

METHODS

We have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fitted distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.

RESULTS

We computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites.

CONCLUSIONS

Establishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

Transmission on empirical dynamic contact networks is influenced by data processing decisions

Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R₀, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.

Analysis of a vector-bias malaria transmission model with application to Mexico, Sudan and Democratic Republic of the Congo

Malaria is a deadly disease transmitted to human through the bite of infected female mosquitoes. The aim of this paper is to study the different vector-bias values between low and high transmission areas with the examples of Mexico (low) and Sudan, Democratic Republic of the Congo (Congo, DR) (high) during malaria transmission. We develop a malaria transmission model with vector-bias and investigate the basic reproduction number, the existence of equilibria and the corresponding globally asymptotically stable. Then, we simulate the reported cases of Mexico and Sudan, Democratic Republic of the Congo by World Health Organization (WHO) (WHO, 0000) and predict the direction of the disease. Our simulation results show that the most endemic country is Congo, DR with the highest vector-bias and R₀ values, followed by Sudan and Mexico with less, respectively and that the disease will die out in Mexico and persist in Sudan and Congo, DR. Furthermore, we perform sensitivity analysis of R₀ and give some useful comments on reducing the cases of the disease.

The impact of transmission dynamics of rabies control: Systematic review

The public health and economic burden of rabies has led to major intersectoral initiatives worldwide to reduce its burden. Over the last decade, the impact of rabies prevention and control programmes in real-world settings has become increasingly evident, especially in countries where most rabies exposures and deaths occur, but they have yet to successfully eradicate rabies due to limited access to health care services. We aimed to systematically review published transmission dynamic modelling studies of rabies in both humans and dogs with a focus on studies which estimated the epidemiological and economic impact of different preventive measures. The findings are intended to inform the World Health Organization's (WHO) Strategic Advisory Group of Experts on Immunization (SAGE) to improve programmatic feasibility and clinical practice in rabies. Medline and Scopus were systematically searched for peer-reviewed articles which were published up to 14^th June 2017. In addition, studies identified from a meeting of the WHO Expert Consultation on Rabies on 26-28^th April 2017 in Bangkok, Thailand were added, resulting in 19 articles which were included in the review. Results from the disease modelling indicated that the basic reproduction number was low (less than 2 in all but one study). All studies found that rabies control through canine vaccination was likely to be effective in terms of reducing the incidence of rabies in dogs and/or humans, with most studies suggesting 70% annual coverage was adequate. Vaccine coverage, dog density and birth rate were identified as crucial factors influencing the effectiveness of the interventions. In conclusion, the findings from this review suggest that rabies control through canine vaccination is likely to be effective in reducing the incidence of rabies. Vaccine coverage, dog density and canine birth rate were identified as critical factors influencing the effectiveness of vaccination interventions.

Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity

BACKGROUND

Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data.

METHODS

We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data.

RESULTS

We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting.

CONCLUSIONS

Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.

Modeling the importation and local transmission of vector-borne diseases in Florida: The case of Zika outbreak in 2016

Chikungunya, dengue, and Zika viruses are all transmitted by Aedes aegypti and Aedes albopictus mosquito species, had been imported to Florida and caused local outbreaks. We propose a deterministic model to study the importation and local transmission of these mosquito-borne diseases. The purpose is to model and mimic the importation of these viruses to Florida via travelers, local infections in domestic mosquitoes by imported travelers, and finally non-travel related transmissions to local humans by infected local mosquitoes. As a case study, the model will be used to simulate the accumulative Zika cases in Florida. Since the disease system is driven by a continuing input of infections from outside sources, orthodox analytic methods based on the calculation of the basic reproduction number are inadequate to describe and predict their behavior. Via steady-state analysis and sensitivity analysis, effective control and prevention measures for these mosquito-borne diseases are tested.

Epidemiology of Mycobacterium bovis and Mycobacterium tuberculosis in animals: Transmission dynamics and control challenges of zoonotic TB in Ethiopia

Mycobacterium tuberculosis complex is the cause of tuberculosis (TB) in humans and other animals. Specifically, Mycobacterium bovis (M. bovis) and Mycobacterium tuberculosis (M. tuberculosis) are highly pathogenic mycobacteria that may infect different animal species and are the sources of TB in humans. The objective of this paper was to review the epidemiology of M. bovis and M. tuberculosis in animals. The review also highlighted the transmission dynamics of M. bovis and M. tuberculosis in humans and animals and control challenges of zoonotic TB in Ethiopia. The literature review focused on scientific peer-reviewed articles from studies exclusively conducted in Ethiopia that were published from 1998 to 2017. Husbandry system, breed and herd size have significant role in the epidemiology of bovine tuberculosis (BTB) in Ethiopia. The information presented reveals that different strains of M. bovis are widely distributed in domestic animals predominantly in the Ethiopian cattle and the main strain was found to be SB1176. In addition, the isolation of M. tuberculosis from domestic animals in different settings signifies the circulation of the agent between humans and animals in Ethiopia. The life styles of the Ethiopian communities, close contact with domestic animals and/or the habit of consuming raw animal products, are suggested as the main factors for transmission of M. bovis and M. tuberculosis between human and animal which may have impact on the TB control program in human. In Ethiopia, a human TB control program has been widely implemented, however, the role of animal in the transmission of the causative agent has been neglected which could be one of the challenges for an effective control program. This warrants the need for incorporating animal TB control programs using "One Health" approach for effective TB control for both human and animal.

Tuberculosis outbreak investigation using phylodynamic analysis

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Phylodynamic analysis gives insight into mycobacterium tuberculosis outbreaks.

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Robust estimation of epidemiological parameters in Bern thanks to high sampling rate.

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Infectious period for WTK cases significantly longer than in Bernese outbreak.

The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis. In this study, we investigate and compare the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. Specifically, we (i) test if the outbreak data sets contain enough genetic variation to estimate short-term evolutionary rates and (ii) reconstruct epidemiological parameters such as the effective reproduction number.

The first outbreak occurred in the Swiss city of Bern (1987–2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.

There is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. Thanks to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, there is much uncertainty in the epidemiological estimates concerning the sparsely sampled (9%) WTK outbreak. Our results suggest that both outbreaks peaked around 1990, although they were only recognized as outbreaks in 1993 (Bern) and 2004 (WTK). Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4–5 years).

Our work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.

The factors affecting household transmission dynamics and community compliance with Ebola control measures: a mixed-methods study in a rural village in Sierra Leone

Background

Little is understood of Ebola virus disease (EVD) transmission dynamics and community compliance with control measures over time. Understanding these interactions is essential if interventions are to be effective in future outbreaks. We conducted a mixed-methods study to explore these factors in a rural village that experienced sustained EVD transmission in Kailahun District, Sierra Leone.

Methods

We reconstructed transmission dynamics using a cross-sectional survey conducted in April 2015, and cross-referenced our results with surveillance, burial, and Ebola Management Centre (EMC) data. Factors associated with EVD transmission were assessed with Cox proportional hazards regression. Following the survey, qualitative semi-structured interviews explored views of community informants and households.

Results

All households (n = 240; 1161 individuals) participated in the survey. 29 of 31 EVD probable/confirmed cases died (93·5% case fatality rate); six deaths (20·6%) had been missed by other surveillance systems. Transmission over five generations lasted 16 weeks. Although most households had ≤5 members there was a significant increase in risk of Ebola in households with > 5 members. Risk of EVD was also associated with older age. Cases were spatially clustered; all occurred in 15 households.

EVD transmission was better understood when the community experience started to concord with public health messages being given. Perceptions of contact tracing changed from invading privacy and selling people to ensuring community safety. Burials in plastic bags, without female attendants or prayer, were perceived as dishonourable. Further reasons for low compliance were low EMC survival rates, family perceptions of a moral duty to provide care to relatives, poor communication with the EMC, and loss of livelihoods due to quarantine. Compliance with response measures increased only after the second generation, coinciding with the implementation of restrictive by-laws, return of the first survivor, reduced contact with dead bodies, and admission of patients to the EMC.

Conclusions

Transmission occurred primarily in a few large households, with prolonged transmission and a high death toll. Return of a survivor to the village and more effective implementation of control strategies coincided with increased compliance to control measures, with few subsequent cases. We propose key recommendations for management of EVD outbreaks based on this experience.

Zika virus dynamics: When does sexual transmission matter?

The Zika virus (ZIKV) has captured worldwide attention with the ongoing epidemic in South America and its link to severe birth defects, most notably microcephaly. ZIKV is spread to humans through a combination of vector and sexual transmission, but the relative contribution of these transmission routes to the overall epidemic remains largely unknown. Furthermore, a disparity in the reported number of infections between males and females has been observed. We develop a mathematical model that describes the transmission dynamics of ZIKV to determine the processes driving the observed epidemic patterns. Our model reveals a 4.8% contribution of sexual transmission to the basic reproductive number, R₀. This contribution is too minor to independently sustain an outbreak but suggests that vector transmission is the main driver of the ongoing epidemic. We also find a minor, yet statistically significant, difference in the mean number of cases in males and females, both at the peak of the epidemic and at equilibrium. While this suggests an intrinsic disparity between males and females, the differences do not account for the vastly greater number of reported cases for females, indicative of a large reporting bias. In addition, we identify conditions under which sexual transmission may play a key role in sparking an epidemic, including temperate areas where ZIKV mosquito vectors are less prevalent.

Assessing the effect of patient screening and isolation on curtailing Clostridium difficile infection in hospital settings

BACKGROUND

Patient screening at the time of hospital admission is not recommended as a routine practice, but may be an important strategy for containment of Clostridium difficile infection (CDI) in hospital settings. We sought to investigate the effect of patient screening in the presence of asymptomatic carriers and in the context of imperfect patient isolation.

METHODS

We developed and parameterized a stochastic simulation model for the transmission dynamics of CDI in a hospital ward.

RESULTS

We found that the transmission of CDI in the hospital, either through asymptomatic carriers or as a results of ineffective implementation of infection control practices, at the time of hospital admission. The results show that, for a sufficiently high reproduction number of CDI, the disease can persist within a hospital setting in the presence of in-ward transmission, even when there are no asymptomatically colonized patients at the time of hospital admission.

CONCLUSIONS

Our findings have significant public health and clinical implications, especially in light of the emergence and community spread of hypervirulent CDI strains with enhanced transmission rates and toxin production. Rapid detection of colonized patients remains an important component of CDI control, especially in the context of asymptomatic transmission. Screening of in-hospital patients with potential exposure to colonized patients or contaminated environment and equipment can help reduce the rates of silent transmission of CDI through asymptomatic carriers.

An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections

BACKGROUND

Host-level influenza virus-respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms' transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens.

METHODS

Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens' natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus-pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed.

RESULTS

This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4-27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength.

CONCLUSIONS

This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses.

Strategies for halting the rise of multidrug resistant TB epidemics: assessing the effect of early case detection and isolation

Background

The increasing rates of multidrug resistant TB (MDR-TB) have posed the question of whether control programs under enhanced directly observed treatment, short-course (DOTS-Plus) are sufficient or implemented optimally. Despite enhanced efforts on early case detection and improved treatment regimens, direct transmission of MDR-TB remains a major hurdle for global TB control.

Methods

We developed an agent-based simulation model of TB dynamics to evaluate the effect of transmission reduction measures on the incidence of MDR-TB. We implemented a 15-day isolation period following the start of treatment in active TB cases. The model was parameterized with the latest estimates derived from the published literature.

Results

We found that if high rates (over 90%) of TB case identification are achieved within 4 weeks of developing active TB, then a 15-day patient isolation strategy with 50% effectiveness in interrupting disease transmission leads to 10% reduction in the incidence of MDR-TB over 10 years. If transmission is fully prevented, the rise of MDR-TB can be halted within 10 years, but the temporal reduction of MDR-TB incidence remains below 20% in this period.

Conclusions

The impact of transmission reduction measures on the TB incidence depends critically on the rates and timelines of case identification. The high costs and adverse effects associated with MDR-TB treatment warrant increased efforts and investments on measures that can interrupt direct transmission through early case detection.

Inferring epidemiological dynamics of infectious diseases using Tajima's D statistic on nucleotide sequences of pathogens

The estimation of the basic reproduction number is essential to understand epidemic dynamics, and time series data of infected individuals are usually used for the estimation. However, such data are not always available. Methods to estimate the basic reproduction number using genealogy constructed from nucleotide sequences of pathogens have been proposed so far. Here, we propose a new method to estimate epidemiological parameters of outbreaks using the time series change of Tajima's D statistic on the nucleotide sequences of pathogens. To relate the time evolution of Tajima's D to the number of infected individuals, we constructed a parsimonious mathematical model describing both the transmission process of pathogens among hosts and the evolutionary process of the pathogens. As a case study we applied this method to the field data of nucleotide sequences of pandemic influenza A (H1N1) 2009 viruses collected in Argentina. The Tajima's D-based method estimated basic reproduction number to be 1.55 with 95% highest posterior density (HPD) between 1.31 and 2.05, and the date of epidemic peak to be 10th July with 95% HPD between 22nd June and 9th August. The estimated basic reproduction number was consistent with estimation by birth-death skyline plot and estimation using the time series of the number of infected individuals. These results suggested that Tajima's D statistic on nucleotide sequences of pathogens could be useful to estimate epidemiological parameters of outbreaks.

Live Eimeria vaccination success in the face of artificial non-uniform vaccine administration in conventionally reared pullets

Live Eimeria vaccines against coccidiosis in poultry initiate immunity using a vaccine dose containing few oocysts; protection is enhanced through subsequent faecal-oral transmission ("cycling") of parasites in the poultry house. Spray-administered Eimeria vaccines can permit wide variations in doses ingested by individual chicks; some chicks may receive no primary vaccination at all. Consequently, protective immunity for the entire flock depends on successful environmental cycling of vaccine progeny. Pullets missing primary vaccination at day of age can become protected from coccidial challenge through cycling of vaccine progeny oocysts from vaccinated (V) cage mates. This study tested whether 40% cage floor coverage (CFC) with a durable material could improve protection against challenge in these "contact-vaccinated" (CV) or successfully V pullets. The six treatment groups tested were CV, V or sham-vaccinated pullets cage-reared on either 0% or 40% CFC. Oocyst output was measured separately for each group for 30 days following vaccine administration. Lesion scores, body weights and total oocyst outputs were measured to quantify protection at 30 days of age against single or mixed Eimeria species challenge infections. Use of 40% CFC to promote low-level oocyst cycling impacted the flock in two ways: (1) more uniform flock immunity was achieved in the 40% CFC (CV similar to V pullets) compared with 0% CFC and (2) protection was enhanced in the 40% CFC compared with the 0% CFC. The use of CFC is an easily adopted means of improving live Eimeria vaccination of caged pullets.

Elimination of visceral leishmaniasis in the Indian subcontinent: a comparison of predictions from three transmission models

We present three transmission models of visceral leishmaniasis (VL) in the Indian subcontinent (ISC) with structural differences regarding the disease stage that provides the main contribution to transmission, including models with a prominent role of asymptomatic infection, and fit them to recent case data from 8 endemic districts in Bihar, India. Following a geographical cross-validation of the models, we compare their predictions for achieving the WHO VL elimination targets with ongoing treatment and vector control strategies. All the transmission models suggest that the WHO elimination target (<1 new VL case per 10,000 capita per year at sub-district level) is likely to be met in Bihar, India, before or close to 2020 in sub-districts with a pre-control incidence of 10 VL cases per 10,000 people per year or less, when current intervention levels (60% coverage of indoor residual spraying (IRS) of insecticide and a delay of 40days from onset of symptoms to treatment (OT)) are maintained, given the accuracy and generalizability of the existing data regarding incidence and IRS coverage. In settings with a pre-control endemicity level of 5/10,000, increasing the effective IRS coverage from 60 to 80% is predicted to lead to elimination of VL 1-3 years earlier (depending on the particular model), and decreasing OT from 40 to 20days to bring elimination forward by approximately 1year. However, in all instances the models suggest that L. donovani transmission will continue after 2020 and thus that surveillance and control measures need to remain in place until the longer-term aim of breaking transmission is achieved.

Experimental and modelling investigations of Opisthorchis viverrini miracidia transmission over time and across temperatures: implications for control

Transmissibility is a significant factor in parasite fitness. The rate and magnitude of parasite transmission affect prevalence and infection intensity in individual hosts and are influenced by environmental factors. In this context, the objectives of this study were: (i) to experimentally assess Opisthorchis viverrini miracidia survival and infectivity over time and across temperatures; and (ii) to combine these experimental results with environmental data to build a key component of a transmission model, identifying seasonal windows of transmission risk in hyper-endemic northeastern Thailand. Five replicates of 50 O. viverrini eggs were randomly distributed and maintained under four temperature conditions (25°C, 30°C, 35°C, 40°C). Microscopic observations were performed on all experimental units over a period of 3months to record miracidia motility and mortality trends. Six infection trials were also conducted to assess infectivity of miracidia over time and across temperatures, using observations of egg hatching success and infection rates. Upon completion of experiments, data were integrated into a transmission model to create a transmission risk index and to simulate seasonal transmission risk. Miracidia survival rate and motility decreased steadily with 50% mortality observed after 2weeks. Hatching and infection success also decreased significantly after 3weeks. Temperatures over 30°C were associated with increased mortality and decreased infectivity. When incorporating local environmental parameters into our model, we observed low transmission risk during the dry season and increasing transmission risk at the onset of the rainy season, culminating with the highest risk in September. We believe that our results provide the first estimates of O. viverrini miracidia survival and transmission potential under variable temperature conditions and suggest that high temperature treatment (>40°C) of fecal waste could be an efficient control strategy.

Modeling the transmission dynamics and control of rabies in China

Human rabies was first recorded in ancient China in about 556 BC and is still one of the major public-health problems in China. From 1950 to 2015, 130,494 human rabies cases were reported in Mainland China with an average of 1977 cases per year. It is estimated that 95% of these human rabies cases are due to dog bites. The purpose of this article is to provide a review about the models, results, and simulations that we have obtained recently on studying the transmission of rabies in China. We first construct a basic susceptible, exposed, infectious, and recovered (SEIR) type model for the spread of rabies virus among dogs and from dogs to humans and use the model to simulate the human rabies data in China from 1996 to 2010. Then we modify the basic model by including both domestic and stray dogs and apply the model to simulate the human rabies data from Guangdong Province, China. To study the seasonality of rabies, in Section 4 we further propose a SEIR model with periodic transmission rates and employ the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010. To understand the spatial spread of rabies, in Section 5 we add diffusion to the dog population in the basic SEIR model to obtain a reaction-diffusion equation model and determine the minimum wave speed connecting the disease-free equilibrium to the endemic equilibrium. Finally, in order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China, in Section 6 we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans and use the two-patch submodel to investigate the rabies virus clades lineages and to simulate the human rabies data from Guizhou and Guangxi, Hebei and Fujian, and Sichuan and Shaanxi, respectively. Some discussions are provided in Section 7.

Influenza A virus in swine breeding herds: Combination of vaccination and biosecurity practices can reduce likelihood of endemic piglet reservoir

Recent modelling and empirical work on influenza A virus (IAV) suggests that piglets play an important role as an endemic reservoir. The objective of this study is to test intervention strategies aimed at reducing the incidence of IAV in piglets and ideally, preventing piglets from becoming exposed in the first place. These interventions include biosecurity measures, vaccination, and management options that swine producers may employ individually or jointly to control IAV in their herds. We have developed a stochastic Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIRV) model that reflects the spatial organization of a standard breeding herd and accounts for the different production classes of pigs therein. Notably, this model allows for loss of immunity for vaccinated and recovered animals, and for vaccinated animals to have different latency and infectious periods from unvaccinated animals as suggested by the literature. The interventions tested include: (1) varied timing of gilt introductions to the breeding herd, (2) gilt separation (no indirect transmission to or from the gilt development unit), (3) gilt vaccination upon arrival to the farm, (4) early weaning, and (5) vaccination strategies of sows with different timing (mass and pre-farrow) and efficacy (homologous vs. heterologous). We conducted a Latin Hypercube Sampling and Partial Rank Correlation Coefficient (LHS-PRCC) analysis combined with a random forest analysis to assess the relative importance of each epidemiological parameter in determining epidemic outcomes. In concert, mass vaccination, early weaning of piglets (removal 0-7days after birth), gilt separation, gilt vaccination, and longer periods between introductions of gilts (6 months) were the most effective at reducing prevalence. Endemic prevalence overall was reduced by 51% relative to the null case; endemic prevalence in piglets was reduced by 74%; and IAV was eliminated completely from the herd in 23% of all simulations. Importantly, elimination of IAV was most likely to occur within the first few days of an epidemic. The latency period, infectious period, duration of immunity, and transmission rate for piglets with maternal immunity had the highest correlation with three separate measures of IAV prevalence; therefore, these are parameters that warrant increased attention for obtaining empirical estimates. Our findings support other studies suggesting that piglets play a key role in maintaining IAV in breeding herds. We recommend biosecurity measures in combination with targeted homologous vaccination or vaccines that provide wider cross-protective immunity to prevent incursions of virus to the farm and subsequent establishment of an infected piglet reservoir.

Evaluation of the presence of Echinococcus granulosus sensu lato in the environment and in hosts in a region endemic for hydatidosis in the province of Chubut (Argentina)

In the province of Chubut (Argentina), hydatidosis is an endemic disease. This province has had a program for control of this disease since 1984. The aim of the present study was to evaluate the presence of Echinococcus granulosus sensu lato in the environment and hosts in a region characterized as endemic and inhabited by indigenous people whose sustenance economy is based on the extensive rearing of sheep. To this end, we carried out an observational, descriptive and transversal study. Sheep sera, water samples, soil samples and canine fecal matter collected from the environment and adult specimens of E. granulosus s.l. were analyzed for strain studies by genotyping. Also there were analyzed serum samples of resident persons to detect E. granulosus antibodies. The frequency of sera positive for hydatidosis was 17.5% in lambs and 28.57% in sheep older than one year of age. The frequency of canine echinococcosis was 25%, and the strain found corresponded to E. granulosus s.l. G1. Samples of canine fecal matter showed presence of taeniid eggs and three of the samples were positive for the coproantigen. The results indicate the circulation of the G1 strain and its presence in the environment under the dry climate conditions of Patagonia. These findings provide useful information for the evaluation of the effectiveness of control actions that may be implemented in a region endemic for hydatidosis.

Development and evaluation of a Markov model to predict changes in schistosomiasis prevalence in response to praziquantel treatment: a case study of Schistosoma mansoni in Uganda and Mali

Background

Understanding whether schistosomiasis control programmes are on course to control morbidity and potentially switch towards elimination interventions would benefit from user-friendly quantitative tools that facilitate analysis of progress and highlight areas not responding to treatment. This study aimed to develop and evaluate such a tool using large datasets collected during Schistosomiasis Control Initiative-supported control programmes.

Methods

A discrete-time Markov model was developed using transition probability matrices parameterized with control programme longitudinal data on Schistosoma mansoni obtained from Uganda and Mali. Four matrix variants (A-D) were used to compare different data types for parameterization: A-C from Uganda and D from Mali. Matrix A used data at baseline and year 1 of the control programme; B used year 1 and year 2; C used baseline and year 1 from selected districts, and D used baseline and year 1 Mali data. Model predictions were tested against 3 subsets of the Uganda dataset: dataset 1, the full 4-year longitudinal cohort; dataset 2, from districts not used to parameterize matrix C; dataset 3, cross-sectional data, and dataset 4, from Mali as an independent dataset.

Results

The model parameterized using matrices A, B and D predicted similar infection dynamics (overall and when stratified by infection intensity). Matrices A-D successfully predicted prevalence in each follow-up year for low and high intensity categories in dataset 1 followed by dataset 2. Matrices A, B and D yielded similar and close matches to dataset 1 with marginal discrepancies when comparing model outputs against datasets 2 and 3. Matrix C produced more variable results, correctly estimating fewer data points.

Conclusion

Model outputs closely matched observed values and were a useful predictor of the infection dynamics of S. mansoni when using longitudinal and cross-sectional data from Uganda. This also held when the model was tested with data from Mali. This was most apparent when modelling overall infection and in low and high infection intensity areas. Our results indicate the applicability of this Markov model approach as countries aim at reaching their control targets and potentially move towards the elimination of schistosomiasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1824-7) contains supplementary material, which is available to authorized users.

Predicting bat colony survival under controls targeting multiple transmission routes of white-nose syndrome

White-nose syndrome (WNS) is a lethal infection of bats caused by the psychrophilic fungus Pseudogymnoascus destructans (Pd). Since the first cases of WNS were documented in 2006, it is estimated that as many as 5.5million bats have succumbed in the United States-one of the fastest mammalian die-offs due to disease ever observed, and the first known sustained epizootic of bats. WNS is contagious between bats, and mounting evidence suggests that a persistent environmental reservoir of Pd plays a significant role in transmission as well. It is unclear, however, the relative contributions of bat-to-bat and environment-to-bat transmission to disease propagation within a colony. We analyze a mathematical model to investigate the consequences of both avenues of transmission on colony survival in addition to the efficacy of disease control strategies. Our model shows that selection of the most effective control strategies is highly dependent on the primary route of WNS transmission. Under all scenarios, however, generalized culling is ineffective and while targeted culling of infected bats may be effective under idealized conditions, it primarily has negative consequences. Thus, understanding the significance of environment-to-bat transmission is paramount to designing effective management plans.

Mechanisms of replacement of circulating viruses by seasonal and pandemic influenza A viruses

BACKGROUND

Seasonal influenza causes annual epidemics by the accumulation of antigenic changes. Pandemic influenza occurs through a major antigenic change of the influenza A virus, which can originate from other hosts. Although new antigenic variants of the influenza A virus replace formerly circulating seasonal and pandemic viruses, replacement mechanisms remain poorly understood.

METHODS

A stochastic individual-based SEIR (susceptible-exposed-infectious-recovered) model with two viral strains (formerly circulating old strain and newly emerged strain) was developed for simulations to elucidate the replacement mechanisms.

RESULTS

Factors and conditions of virus and host populations affecting the replacement were identified. Replacement is more likely to occur in tropical regions than temperate regions. The magnitude of the ongoing epidemic by the old strain, herd immunity against the old strain, and timing of appearance of the new strain are not that important for replacement. It is probable that the frequency of replacement by a pandemic virus is higher than a seasonal virus because of the high initial susceptibility and high basic reproductive number of the pandemic virus.

CONCLUSIONS

The findings of this study on replacement mechanisms could lead to a better understanding of virus transmission dynamics and may possibly be helpful in establishing an effective strategy to mitigate the impact of seasonal and pandemic influenza.

Landscape ecology of Trypanosoma cruzi in the southern Yucatan Peninsula

Landscape interactions of Trypanosoma cruzi (Tc) with Triatoma dimidiata (Td) depend on the presence and relative abundance of mammal hosts. This study analyzed a landscape adjacent to the Calakmul Biosphere Reserve, composed of conserved areas, crop and farming areas, and the human community of Zoh Laguna with reported Chagas disease cases. Sylvatic mammals of the Chiroptera, Rodentia, and Marsupialia orders were captured, and livestock and pets were sampled along with T. dimidiata in all habitats. Infection by T. cruzi was analyzed using mtDNA markers, while lineage and DTU was analyzed using the mini-exon. 303 sylvatic specimens were collected, corresponding to 19 species during the rainy season and 114 specimens of 18 species during dry season. Five bats Artibeus jamaicensis, Artibeus lituratus, Sturnira lilium, Sturnira ludovici, Dermanura phaeotis (Dp) and one rodent Heteromys gaumeri were collected in the three habitats. All but Dp, and including Carollia brevicauda and Myotis keaysi, were infected with predominately TcI in the sylvatic habitat and TcII in the ecotone. Sigmodon hispidus was the rodent with the highest prevalence of infection by T. cruzi I and II in ecotone and domestic habitats. Didelphis viginiana was infected only with TcI in both domestic and sylvatic habitats; the only two genotyped human cases were TcII. Two main clades of T. cruzi, lineages I (DTU Ia) and II (DTU VI), were found to be sympatric (all habitats and seasons) in the Zoh-Laguna landscape, suggesting that no species-specific interactions occur between the parasite and any mammal host, in any habitat. We have also found mixed infections of the two principal T. cruzi clades in individuals across modified habitats, particularly in livestock and pets, and in both haplogroups of T. dimidiata. Results are contradictory to the dilution hypothesis, although we did find that most resilient species had an important role as T. cruzi hosts. Our study detected some complex trends in parasite transmission related to lineage sorting within the matrix. Intriguingly, TcIa is dominant in terrestrial small wildlife in the sylvatic habitat and is the only parasite DTU found in D. virginiana in the domestic habitat, although its frequency remained constant in sylvatic and ecotone vectors. Bats have a key role in TcVI dispersal from the sylvatic habitat, while dogs, sheep, and humans are drivers of TcVI between domestic and ecotone habitats. Overall, our results allow us to conclude that T. cruzi transmission is dependent on host availability within a highly permeable landscape in Zoh Laguna.