Decline of Marteilia cochillia in Ría de Arousa may be due to increased resistance in host Cerastoderma edule

A huge, unprecedented mortality of cockle Cerastoderma edule caused by the protist Marteilia cochillia, which had never before been detected in Galicia (NW Spain), brought on a cockle fishery collapse in the Ría de Arousa (Galicia) in 2012. Since then, the disease dynamic pattern in the shellfish bed of Lombos do Ulla (at the inner area of that ria) involved an overwhelming annual wave of infections and subsequent cockle mass mortality that caused the near extinction of every cohort recruited to that bed. However, a pattern shift was detected among wild cohorts recruiting since 2016, with progressive declines of marteiliosis prevalence and increments in cockle survival. This suggested 2 non-exclusive hypotheses: increasing marteiliosis resistance through natural selection, and reduced abundance and/or virulence of the parasite. A field experiment was performed to assess these hypotheses by comparing marteiliosis prevalence and severity, as well as mortality, in cockles that naturally recruited to this bed in 2017 and 2018 with those of naïve cockles collected from a marteiliosis-free area and transplanted into Lombos do Ulla in 2017 and 2018. Marteiliosis prevalence and cumulative cockle mortality quickly reached very high values among the transplanted cockles, demonstrating that the parasite remained present and virulent in the area. Conversely, marteiliosis prevalence and cockle mortality were much lower in the cockles that recruited to Lombos do Ulla, suggesting increased resistance that may have been driven by natural selection. The young age at which cockles start reproduction and the very high mortality caused by marteiliosis may have enhanced natural selection.

Modelling the effect of travel-related policies on disease control in a meta-population structure

Travel restrictions, while delaying the spread of an emerging disease from the source, could inflict substantial socioeconomic burden. Travel-related policies, such as quarantine and testing of travelers, may be considered as alternative strategies to mitigate the negative impact of travel bans. We developed a meta-population, delay-differential model to evaluate a strategy that combines testing of travelers prior to departure from the source of infection with quarantine and testing at exit from quarantine in the destination population. Our results, based on early parameter estimates of SARS-CoV-2 infection, indicate that testing travelers at exit from quarantine is more effective in delaying case importation than testing them before departure or upon arrival. We show that a 1-day quarantine with an exit test could outperform a longer, 3-day quarantine without testing in delaying the outbreak peak. Rapid, large-scale testing capacities with short turnaround times provide important means of detecting infectious cases and reducing case importation, while shortening quarantine duration for travelers at destination.

On the treatment of melanoma: A mathematical model of oncolytic virotherapy

We develop and analyze a mathematical model of oncolytic virotherapy in the treatment of melanoma. We begin with a special, local case of the model, in which we consider the dynamics of the tumour cells in the presence of an oncolytic virus at the primary tumour site. We then consider the more general regional model, in which we incorporate a linear network of lymph nodes through which the tumour cells and the oncolytic virus may spread. The modelling also considers the impact of hypoxia on the disease dynamics. The modelling takes into account both the effects of hypoxia on tumour growth and spreading, as well as the impact of hypoxia on oncolytic virotherapy as a treatment modality. We find that oxygen-rich environments are favourable for the use of adenoviruses as oncolytic agents, potentially suggesting the use of complementary external oxygenation as a key aspect of treatment. Furthermore, the delicate balance between a virus' infection capabilities and its oncolytic capabilities should be considered when engineering an oncolytic virus. If the virus is too potent at killing tumour cells while not being sufficiently effective at infecting them, the infected tumour cells are destroyed faster than they are able to infect additional tumour cells, leading less favourable clinical results. Numerical simulations are performed in order to support the analytic results and to further investigate the impact of various parameters on the outcomes of treatment. Our modelling provides further evidence indicating the importance of three key factors in treatment outcomes: tumour microenvironment oxygen concentration, viral infection rates, and viral oncolysis rates. The numerical results also provide some estimates on these key model parameters which may be useful in the engineering of oncolytic adenoviruses.

Epidemiological impacts of attenuated African swine fever virus circulating in wild boar populations

African swine fever virus (ASFV) genotype II has been present in wild boar in the European Union since 2014. Control measures have reduced the incidence of the ASF, but highly virulent as well as attenuated ASFV strains continue to circulate. We present the intraherd epidemiological parameters of low and highly virulent ASFV in wild boar from experimental data, and for the first time, evaluate the impact of attenuated strain circulation through unique deterministic compartmental model simulations under various potential scenarios and hypotheses. Using an estimated PCR infectious threshold of TPCR = 36.4, we obtained several transmission parameters, like an Rx (experimental intraherd R0) value of 4.5. We also introduce two novel epidemiological parameters: infectious power and resistance power, which indicate the ability of animals to transmit the infection and the reduction in infectiousness after successive exposures to varying virulence strains, respectively. The presence of ASFV attenuated strains results in 4-17% of animals either remaining in a carrier state or becoming susceptible again when exposed to highly virulent ASFV for more than two years. The timing between exposures to viruses of different virulence also influences the percentage of animals that die or remain susceptible. The findings of this study can be utilized in epidemiological modelling and provide insight into important risk situations that should be considered for surveillance and future potential ASF vaccination strategies in wild boar.

Seasonal variations of intensity of avian malaria infection in the Thousand Island Lake System, China

BACKGROUND

Migratory birds play an important part in the spread of parasites, with more or less impact on resident birds. Previous studies focus on the prevalence of parasites, but changes in infection intensity over time have rarely been studied. As infection intensity can be quantified by qPCR, we measured infection intensity during different seasons, which is important for our understanding of parasite transmission mechanisms.

METHODS

Wild birds were captured at the Thousand Island Lake with mist nets and tested for avian hemosporidiosis infections using nested PCR. Parasites were identified using the MalAvi database. Then, we used qPCR to quantify the infection intensity. We analyzed the monthly trends of intensity for all species and for different migratory status, parasite genera and sexes.

RESULTS

Of 1101 individuals, 407 were infected (37.0%) of which 95 were newly identified and mainly from the genus Leucocytozoon. The total intensity trend shows peaks at the start of summer, during the breeding season of hosts and during the over-winter season. Different parasite genera show different monthly trends. Plasmodium causes high prevalence and infection intensity of winter visitors. Female hosts show significant seasonal trends of infection intensity.

CONCLUSIONS

The seasonal changes of infection intensity is consistent with the prevalence. Peaks occur early and during the breeding season and then there is a downward trend. Spring relapses and avian immunity are possible reasons that could explain this phenomenon. In our study, winter visitors have a higher prevalence and infection intensity, but they rarely share parasites with resident birds. This shows that they were infected with Plasmodium during their departure or migration and rarely transmit the disease to resident birds. The different infection patterns of different parasite species may be due to vectors or other ecological properties.

Between lives and economy: COVID-19 containment policy in open economies

This paper studies containment policies for combating a pandemic in an open-economy context. It does so via quantitative analyses using a model that incorporates a standard epidemiological compartmental model in a general equilibrium multi-country, multi-sector Ricardian model of international trade with input-output linkages. We quantitatively evaluate the long-run welfare and real-income losses due to the short-run pandemic shocks, and we study the role of trade in these effects. We devise a novel approach to computing national optimal policies. We find that (1) the long-run welfare and real-income losses due to just two years of pandemic shocks are substantial; (2) international trade helps buffer both the welfare and real-income losses, and it also saves lives; (3) the computed optimal policies indicate that most countries should have tightened their containment measures relative to what was done; and (4) compared to the case of autarky, the optimal policy under trade is generally more stringent.

Community incidence patterns drive the risk of SARS-CoV-2 outbreaks and alter intervention impacts in a high-risk institutional setting

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings.

Anthropogenic subsidies influence resource use during a mange epizootic in a desert coyote population

Colonization of urban areas by synanthropic wildlife introduces novel and complex alterations to established ecological processes, including the emergence and spread of infectious diseases. Aggregation at urban resources can increase disease transfer, with wide-ranging species potentially infecting outlying populations. The garrison at the National Training Center, Fort Irwin, California, USA, was recently colonized by mange-infected coyotes (Canis latrans) that also use the surrounding Mojave Desert. This situation provided an ideal opportunity to examine the effects of urban resources on disease dynamics. We evaluated seasonal space use and determined the influence of anthropogenic subsidies, water sources, and prey density on urban resource selection. We found no difference in home range size between healthy and infected individuals, but infected residents had considerably more spatial overlap with one another than healthy residents. All coyotes selected for anthropogenic subsidies during all seasons, while infected coyotes seasonally selected for urban water sources, and healthy coyotes seasonally selected for urban areas with greater densities of natural prey. These results suggest that while all coyotes were selecting for anthropogenic subsidies, infected resident coyotes demonstrated a greater tolerance for other conspecifics, which could be facilitating the horizontal transfer of sarcoptic mange to non-resident coyotes. Conversely, healthy coyotes also selected for natural prey and healthy residents exhibited a lack of spatial overlap with other coyotes suggesting they were not reliant on anthropogenic subsidies and were maintaining territories. Understanding the association between urban wildlife, zoonotic diseases, and urban resources can be critical in determining effective responses for mitigating future epizootics.

The impact of travelling on the COVID-19 infection cases in Germany

BACKGROUND

COVID-19 continues to disrupt social lives and the economy of many countries and challenges their healthcare capacities. Looking back at the situation in Germany in 2020, the number of cases increased exponentially in early March. Social restrictions were imposed by closing e.g. schools, shops, cafés and restaurants, as well as borders for travellers. This reaped success as the infection rate descended significantly in early April. In mid July, however, the numbers started to rise again. Of particular reasons was that from mid June onwards, the travel ban has widely been cancelled or at least loosened. We aim to measure the impact of travellers on the overall infection dynamics for the case of (relatively) few infectives and no vaccinations available. We also want to analyse under which conditions political travelling measures are relevant, in particular in comparison to local measures. By travel restrictions in our model we mean all possible measures that equally reduce the possibility of infected returnees to further spread the disease in Germany, e.g. travel bans, lockdown, post-arrival tests and quarantines.

METHODS

To analyse the impact of travellers, we present three variants of an susceptible-exposed-infected-recovered-deceased model to describe disease dynamics in Germany. Epidemiological parameters such as transmission rate, lethality, and detection rate of infected individuals are incorporated. We compare a model without inclusion of travellers and two models with a rate measuring the impact of travellers incorporating incidence data from the Johns Hopkins University. Parameter estimation was performed with the aid of the Monte-Carlo-based Metropolis algorithm. All models are compared in terms of validity and simplicity. Further, we perform sensitivity analyses of the model to observe on which of the model parameters show the largest influence the results. In particular, we compare local and international travelling measures and identify regions in which one of these shows larger relevance than the other.

RESULTS

In the comparison of the three models, both models with the traveller impact rate yield significantly better results than the model without this rate. The model including a piecewise constant travel impact rate yields the best results in the sense of maximal likelihood and minimal Bayesian Information Criterion. We synthesize from model simulations and analyses that travellers had a strong impact on the overall infection cases in the considered time interval. By a comparison of the reproductive ratios of the models under traveller/no-traveller scenarios, we found that higher traveller numbers likely induce higher transmission rates and infection cases even in the further course, which is one possible explanation to the start of the second wave in Germany as of autumn 2020. The sensitivity analyses show that the travelling parameter, among others, shows a larger impact on the results. We also found that the relevance of travel measures depends on the value of the transmission parameter: In domains with a lower transmission parameter, caused either by the current variant or local measures, it is found that handling the travel parameters is more relevant than those with lower value of the transmission.

CONCLUSIONS

We conclude that travellers is an important factor in controlling infection cases during pandemics. Depending on the current situation, travel restrictions can be part of a policy to reduce infection numbers, especially when case numbers and transmission rate are low. The results of the sensitivity analyses also show that travel measures are more effective when the local transmission is already reduced, so a combination of those two appears to be optimal. In any case, supervision of the influence of travellers should always be undertaken, as another pandemic or wave can happen in the upcoming years and vaccinations and basic hygiene rules alone might not be able to prevent further infection waves.

Presence of invasive American bullfrogs may reduce infectious disease in a native frog species

Amphibians breeding in aquatic environments may encounter a myriad of threats during their life cycle. One species known to prey on native amphibians in aquatic habitats is the invasive North American bullfrog Lithobates catesbeianus, which, besides being a voracious predator and competitor, often acts as a pathogen carrier and disease superspreader because it tolerates high infection loads of the frog-killing fungus Batrachochytrium dendrobatidis (Bd). Here, we hypothesized that the presence of the bullfrogs in microcosms should either (1) decrease Bd disease severity in native frog species by discouraging them from using the aquatic environment, or (2) increase the mortality of the native species. We tested these 2 mutually exclusive hypotheses by co-housing the snouted treefrog Scinax x-signatus (native to our study area) with L. catesbeianus in the laboratory, exposing them to Bd, and using qPCR analysis to quantify the resulting Bd infection loads in the native frogs. Our experiment had the following replicated treatments: (1) native-only treatment (3 individuals of S. x-signatus), (2) native-predominant treatment (2 S. x-signatus + 1 L. catesbeianus), and (3) exotic-predominant treatment (1 S. x-signatus + 2 L. catesbeianus). We found that Bd infection loads in the native S. x-signatus were highest in the native-only treatment, and lowest in the exotic-predominant treatment, indicating that bullfrogs may discourage native frogs from occupying the aquatic habitat, thus reducing encounter rates between native frogs and the waterborne pathogen. This effect could be driven by the bullfrogs' predatory behavior and their high philopatry to aquatic habitats. Our results highlight that predation risk adds to the complexity of host-species interactions in Bd epidemiology.

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2-4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate infection incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.

Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2-4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate infection incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.

Host-multiparasite interactions in amphibians: a review

Parasites, including viruses, bacteria, fungi, protists, helminths, and arthropods, are ubiquitous in the animal kingdom. Consequently, hosts are frequently infected with more than one parasite species simultaneously. The assessment of such co-infections is of fundamental importance for disease ecology, but relevant studies involving non-domesticated animals have remained scarce. Many amphibians are in decline, and they generally have a highly diverse parasitic fauna. Here we review the literature reporting on field surveys, veterinary case studies, and laboratory experiments on co-infections in amphibians, and we summarize what is known about within-host interactions among parasites, which environmental and intrinsic factors influence the outcomes of these interactions, and what effects co-infections have on hosts. The available literature is piecemeal, and patterns are highly diverse, so that identifying general trends that would fit most host–multiparasite systems in amphibians is difficult. Several examples of additive, antagonistic, neutral, and synergistic effects among different parasites are known, but whether members of some higher taxa usually outcompete and override the effects of others remains unclear. The arrival order of different parasites and the time lag between exposures appear in many cases to fundamentally shape competition and disease progression. The first parasite to arrive can gain a marked reproductive advantage or induce cross-reaction immunity, but by disrupting the skin and associated defences (i.e., skin secretions, skin microbiome) and by immunosuppression, it can also pave the way for subsequent infections. Although there are exceptions, detrimental effects to the host are generally aggravated with increasing numbers of co-infecting parasite species. Finally, because amphibians are ectothermic animals, temperature appears to be the most critical environmental factor that affects co-infections, partly via its influence on amphibian immune function, partly due to its direct effect on the survival and growth of parasites. Besides their importance for our understanding of ecological patterns and processes, detailed knowledge about co-infections is also crucial for the design and implementation of effective wildlife disease management, so that studies concentrating on the identified gaps in our understanding represent rewarding research avenues.

Using sensitivity analysis to identify factors promoting higher versus lower infection prevalence in multi-host communities

Relationships between host species richness and levels of disease in a focal host are likely to be context-dependent, depending on the characteristics of which particular host species are present in a community. I use a multi-host epidemiological model with environmental transmission to explore how the characteristics of the host species (e.g., competence and competitive ability), host density, and the pathogen transmission mechanism affect the proportion of infected individuals (i.e., infection prevalence) in a focal host. My sensitivity-based approach identifies the indirect pathways through which specific ecological and epidemiological processes affect focal host infection prevalence. This in turn yields predictions about the context-dependent rules governing whether increased host species richness increases (amplifies) or decreases (dilutes) infection prevalence in a focal host. For example, in many cases, amplification and dilution are predicted to occur when added host species are sources or sinks of infectious propagules, respectively. However, if the added host species have strong and asymmetric competitive effects on resident host species, then amplification and dilution are predicted to occur when the added host species have stronger competitive effects on resident host species that are sources or sinks of infectious propagules, respectively. My results also predict that greater dilution and less amplification is more likely to occur under frequency-dependent direct transmission than density-dependent direct transmission when (i) the added hosts have lower competence than resident host species and (ii) interspecific competition between the added host species and resident host species is lower; the opposite conditions promote greater amplification and less dilution under frequency-dependent direct transmission. This work helps identify and explain the mechanisms shaping the context-dependent relationships between host species richness and disease in multi-host communities.

Disease dynamics and mean field models for clustered networks

Social networks are clustered networks with short mean path length. In this work we analyze the disease dynamics in a class of this type of small-world networks composed of set of households and a set of workplaces. Individuals from each household are randomly assigned to workplaces. In both environments we assumed complete mixing and therefore we obtain highly clustered networks with short mean path lengths. Basic reproduction numbers were computed numerically and we show that at endemic equilibrium the average susceptible proportion <S/N> is different from the inverse of the basic reproduction number (R₀^-1). Therefore exist an exponent p≠1 for which <S/N>^p=R₀^-1. Using this exponent we developed a mean field model which closely capture the disease dynamics in the network. Finally we outline how this model could be use to model vector-borne diseases in social networks.

Evaluating effects of different control strategies for Infectious Salmon Anaemia (ISA) in marine salmonid farming by scenario simulation using a disease transmission model

Infectious salmon anaemia (ISA) is an important viral disease causing economic losses and reduced welfare in farmed Atlantic salmon. Here, we present a spatio-temporal stochastic model for the spread of ISA between and within marine aquaculture farms. The model is estimated on historical production data for all marine salmonid farms in Norway from 2004 to February 2019. In this time 142 outbreaks of ISA occurred. We find that transmission from infected neighbouring farms accounts for around 50% of the infections, whereas transmission from "non-specified sources" accounts for around 40%. We hypothesise that the most important of the latter are viruses mutating from the non-virulent ISAV HPR0 to the virulent ISAV HPRdel. The model is used for scenario simulation, or what-if analysis, to investigate the effects of potential strategies to combat ISA, including screening, vaccination and culling. Changing from the current strategy of culling farms with detected ISA-outbreaks to mandatory screening and culling when virus is detected will reduce the fraction of cohorts with a clinical ISA outbreak from 3.8 to 0.36%. Introducing mandatory vaccination would have approximately the same effect as the current stamping-out strategy. The scenario simulation is a useful tool for deciding on appropriate mitigation measures.

The role of social structure and dynamics in the maintenance of endemic disease

Social interactions are required for the direct transmission of infectious diseases. Consequently, the social network structure of populations plays a key role in shaping infectious disease dynamics. A huge research effort has examined how specific social network structures make populations more (or less) vulnerable to damaging epidemics. However, it can be just as important to understand how social networks can contribute to endemic disease dynamics, in which pathogens are maintained at stable levels for prolonged periods of time. Hosts that can maintain endemic disease may serve as keystone hosts for multi-host pathogens within an ecological community, and also have greater potential to act as key wildlife reservoirs of agricultural and zoonotic diseases. Here, we examine combinations of social and demographic processes that can foster endemic disease in hosts. We synthesise theoretical and empirical work to demonstrate the importance of both social structure and social dynamics in maintaining endemic disease. We also highlight the importance of distinguishing between the local and global persistence of infection and reveal how different social processes drive variation in the scale at which infectious diseases appear endemic. Our synthesis provides a framework by which to understand how sociality contributes to the long-term maintenance of infectious disease in wildlife hosts and provides a set of tools to unpick the social and demographic mechanisms involved in any given host-pathogen system.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00265-021-03055-8.

Heterogeneity in SIR epidemics modeling: superspreaders and herd immunity

Deterministic epidemic models, such as the Susceptible-Infected-Recovered (SIR) model, are immensely useful even if they lack the nuance and complexity of social contacts at the heart of network science modeling. Here we present a simple modification of the SIR equations to include the heterogeneity of social connection networks. A typical power-law model of social interactions from network science reproduces the observation that individuals with a high number of contacts, "hubs" or "superspreaders", can become the primary conduits for transmission. Conversely, once the tail of the distribution is saturated, herd immunity sets in at a smaller overall recovered fraction than in the analogous SIR model. The new dynamical equations suggest that cutting off the tail of the social connection distribution, i.e., stopping superspreaders, is an efficient non-pharmaceutical intervention to slow the spread of a pandemic, such as the Coronavirus Disease 2019 (COVID-19).

An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast

We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R 0) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R 0. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.

An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast

We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R ₀) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R ₀. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.

Prophylactic host behaviour discourages pathogen exploitation

Much work has considered the evolution of pathogens, but little is known about how they respond to changes in host behaviour. We build a model of sublethal disease effects where hosts are able to choose to engage in prophylactic measures that reduce the likelihood of disease transmission. This choice is mediated by utility costs and benefits associated with prophylaxis, and the fraction of hosts engaged in prophylaxis is also affected by population dynamics. When prophylactic host behaviour occurs, we find that the level of pathogen host exploitation is reduced, by the action of selection, relative to the level that would otherwise be predicted in the absence of prophylaxis. Our work emphasizes the significance of the transmission-recovery trade-off faced by the pathogen and the ability of the pathogen to influence host prophylactic behaviour.

Estimating the impact of lock-down, quarantine and sensitization in a COVID-19 outbreak: lessons from the COVID-19 outbreak in China

In recent history, COVID-19 is one of the worst infectious disease outbreaks currently affecting humanity globally. Using real data on the COVID-19 outbreak from 22 January 2020 to 30 March 2020, we developed a mathematical model to investigate the impact of control measures in reducing the spread of the disease. Analyses of the model were carried out to determine the dynamics. The results of the analyses reveal that, using the data from China, implementing all possible control measures best reduced the rate of secondary infections. However, quarantine (isolation) of infectious individuals is shown to have the most dominant effect. This possibility emphasizes the need for extensive testing due to the possible prevalence of asymptomatic COVID-19 cases.

Going by the numbers : Learning and modeling COVID-19 disease dynamics

The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern.

Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models

We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction-diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with 95 % CI 6802-7204. The model is validated for the total deceased cases, however, is found to be invalid for the total infected cases. We discuss possible improvements of the model.

Early stage COVID-19 disease dynamics in Germany: models and parameter identification

Since the end of 2019 an outbreak of a new strain of coronavirus, called SARS-CoV-2, is reported from China and later other parts of the world. Since January 21, World Health Organization (WHO) reports daily data on confirmed cases and deaths from both China and other countries (www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports). The Johns Hopkins University (github.com/CSSEGISandData/COVID-19/blob/master/csse_COVID_19_data/csse_COVID_19_time_series/time_series_COVID19_confirmed_global.csv) collects those data from various sources worldwide on a daily basis. For Germany, the Robert-Koch-Institute (RKI) also issues daily reports on the current number of infections and infection related fatal cases (www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html). However, due to delays in the data collection, the data from RKI always lags behind those reported by Johns Hopkins. In this work we present an extended SEIRD-model to describe the disease dynamics in Germany. The parameter values are identified by matching the model output to the officially reported cases. An additional parameter to capture the influence of unidentified cases is also included in the model.

Dynamics in treatment response and disease progression of metastatic colorectal cancer (mCRC) patients with focus on BRAF status and primary tumor location: analysis of untreated RAS-wild-type mCRC patients receiving FOLFOXIRI either with or without panitumumab in the VOLFI trial (AIO KRK0109)

PURPOSE

In mCRC, disease dynamics may play a critical role in the understanding of long-term outcome. We evaluated depth of response (DpR), time to DpR, and post-DpR survival as relevant endpoints.

METHODS

We analyzed DpR by central review of computer tomography images (change from baseline to smallest tumor diameter), early tumor shrinkage (≥ 20% reduction in tumor diameter at first reassessment), time to DpR (study randomization to DpR-image), post-DpR progression-free survival (pPFS = DpR-image to tumor progression or death), and post-DpR overall survival (pOS = DpR-image to death) with special focus on BRAF status in 66 patients and primary tumor site in 86 patients treated within the VOLFI-trial, respectively.

RESULTS

BRAF wild-type (BRAF-WT) compared to BRAF mutant (BRAF-MT) patients had greater DpR (- 57.6% vs. - 40.8%, p = 0.013) with a comparable time to DpR [4.0 (95% CI 3.1-4.4) vs. 3.9 (95% CI 2.5-5.5) months; p = 0.8852]. pPFS was 6.5 (95% CI 4.9-8.0) versus 2.6 (95% CI 1.2-4.0) months in favor of BRAF-WT patients (HR 0.24 (95% CI 0.11-0.53); p < 0.001). This transferred into a significant difference in pOS [33.6 (95% CI 26.0-41.3) vs. 5.4 (95% CI 5.0-5.9) months; HR 0.27 (95% CI 0.13-0.55); p < 0.001]. Similar observations were made for patients stratified for primary tumor site.

CONCLUSIONS

BRAF-MT patients derive a less profound treatment response compared to BRAF-WT patients. The difference in outcome according to BRAF status is evident after achievement of DpR with BRAF-MT patients hardly deriving any further disease control beyond DpR. Our observations hint towards an aggressive tumor evolution in BRAF-MT tumors, which may already be molecularly detectable at the time of DpR.

Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model

Background

Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic.

Methods

We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection.

Results

The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4.

Conclusions

Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic.

The influence of awareness on epidemic spreading on random networks

During an outbreak, the perceived infection risk of an individual affects his/her behavior during an epidemic to lower the risk. We incorporate the awareness of infection risk into the Volz-Miller SIR epidemic model, to study the effect of awareness on disease dynamics. We consider two levels of awareness, the local one represented by the prevalence among the contacts of an individual, and the global one represented by the prevalence in the population. We also consider two possible effects of awareness: reducing infection rate or breaking infectious edges. We use the next generation matrix method to obtain the basic reproduction number of our models, and show that awareness acquired during an epidemic does not affect the basic reproduction number. However, awareness acquired from outbreaks in other regions before the start of the local epidemic reduces the basic reproduction number. Awareness always reduces the final size of an epidemic. Breaking infectious edges causes a larger reduction than reducing the infection rate. If awareness reduces the infection rate, the reduction increases with both local and global awareness. However, if it breaks infectious edges, the reduction may not be monotonic. For the same awareness, the reduction may reach a maximum on some intermediate infection rates. Whether local or global awareness has a larger effect on reducing the final size depends on the network degree distribution and the infection rate.

Population Dynamics of Bank Voles Predicts Human Puumala Hantavirus Risk

Predicting risk of zoonotic diseases, i.e., diseases shared by humans and animals, is often complicated by the population ecology of wildlife host(s). We here demonstrate how ecological knowledge of a disease system can be used for early prediction of human risk using Puumala hantavirus (PUUV) in bank voles (Myodes glareolus), which causes Nephropathia epidemica (NE) in humans, as a model system. Bank vole populations at northern latitudes exhibit multiannual fluctuations in density and spatial distribution, a phenomenon that has been studied extensively. Nevertheless, existing studies predict NE incidence only a few months before an outbreak. We used a time series on cyclic bank vole population density (1972-2013), their PUUV infection rates (1979-1986; 2003-2013), and NE incidence in Sweden (1990-2013). Depending on the relationship between vole density and infection prevalence (proportion of infected animals), either overall density of bank voles or the density of infected bank voles may be used to predict seasonal NE incidence. The density and spatial distribution of voles at density minima of a population cycle contribute to the early warning of NE risk later at its cyclic peak. When bank voles remain relatively widespread in the landscape during cyclic minima, PUUV can spread from a high baseline during a cycle, culminating in high prevalence in bank voles and potentially high NE risk during peak densities.

Adaptations and phenotypic plasticity in developmental traits of Marshallagia marshalli

Despite the economic, social and ecological importance of the ostertagiine abomasal nematode Marshallagia marshalli, little is known about its life history traits and its adaptations to cope with environmental extremes. Conserved species-specific traits can act as exaptations that may enhance parasite fitness in changing environments. Using a series of experiments, we revealed several unique adaptations of the free-living stages of M. marshalli that differ from other ostertagiines. Eggs were isolated from the feces of bighorn sheep (Ovis canadensis) from the Canadian Rocky Mountains and were cultured at different temperatures and with different media. Hatching occurred primarily as L1s in an advanced stage of development, morphologically very similar to a L2. When cultured at 20 °C, however, 2.86% of eggs hatched as L3, with this phenomenon being significantly more common at higher temperatures, peaking at 30 °C with 28.95% of eggs hatching as L3s. After hatching, free-living larvae of M. marshalli did not feed nor grow as they matured from L1 to infective L3. These life history traits seem to be adaptations to cope with the extreme environmental conditions that Marshallagia faces across its extensive latitudinal distribution in North America and Eurasia. In order to refine the predictions of parasite dynamics under scenarios of a changing climate, basic life history traits and temperature-dependent phenotypic behaviour should be incorporated into models for parasite biology.

Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model

Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate statistical inference procedure. We adopt a mechanistic-statistical approach grounded on a coupled reaction-diffusion-absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France.

Reservoir frogs: seasonality of Batrachochytrium dendrobatidis infection in robber frogs in Dominica and Montserrat

Emerging infectious diseases are an increasingly important threat to wildlife conservation, with amphibian chytridiomycosis, caused by Batrachochytrium dendrobatidis, the disease most commonly associated with species declines and extinctions. However, some amphibians can be infected with B. dendrobatidis in the absence of disease and can act as reservoirs of the pathogen. We surveyed robber frogs (Eleutherodactylus spp.), potential B. dendrobatidis reservoir species, at three sites on Montserrat, 2011-2013, and on Dominica in 2014, to identify seasonal patterns in B. dendrobatidis infection prevalence and load (B. dendrobatidis genomic equivalents). On Montserrat there was significant seasonality in B. dendrobatidis prevalence and B. dendrobatidis load, both of which were correlated with temperature but not rainfall. B. dendrobatidis prevalence reached 35% in the cooler, drier months but was repeatedly undetectable during the warmer, wetter months. Also, B. dendrobatidis prevalence significantly decreased from 53.2% when the pathogen emerged on Montserrat in 2009 to a maximum 34.8% by 2011, after which it remained stable. On Dominica, where B. dendrobatidis emerged seven years prior to Montserrat, the same seasonal pattern was recorded but at lower prevalence, possibly indicating long-term decline. Understanding the dynamics of disease threats such as chytridiomycosis is key to planning conservation measures. For example, reintroductions of chytridiomycosis-threatened species could be timed to coincide with periods of low B. dendrobatidis infection risk, increasing potential for reintroduction success.

GEOFIL: A spatially-explicit agent-based modelling framework for predicting the long-term transmission dynamics of lymphatic filariasis in American Samoa

In this study, a spatially-explicit agent-based modelling framework GEOFIL was developed to predict lymphatic filariasis (LF) transmission dynamics in American Samoa. GEOFIL included individual-level information on age, gender, disease status, household location, household members, workplace/school location and colleagues/schoolmates at each time step during the simulation. In American Samoa, annual mass drug administration from 2000 to 2006 successfully reduced LF prevalence dramatically. However, GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. The microfilaraemia and antigenaemia prevalence in 6-7-yo children were much lower than in the overall population. Mosquito biting rates were found to be a critical determinant of infection risk. Transmission hotspots are likely to disappear with lower biting rates. GEOFIL highlights current knowledge gaps, such as data on mosquito abundance, biting rates and within-host parasite dynamics, which are important for improving the accuracy of model predictions.

Disentangling causes of seasonal infection prevalence patterns: tropical tadpoles and chytridiomycosis as a model system

Identifying the factors that affect pathogen prevalence is critical to understanding the effects of wildlife diseases. We aimed to examine drivers of seasonal changes in the prevalence of infection by the amphibian chytrid fungus Batrachochytrium dendrobatidis in tadpoles. Because tadpoles may be important reservoirs for this disease, examining them will aid in understanding how chytridiomycosis affects entire amphibian populations. We hypothesized that temperature is a strong driver of prevalence of Bd in tadpoles, and the accumulation of infection as tadpoles become larger and older also drives prevalence in this system. We studied Litoria rheocola, a tropical rainforest stream frog with seasonal recruitment of annual tadpoles, and surveyed 6 streams in northeastern Queensland, Australia. Comparisons among models relating infection status to stream type, season, their interaction, tadpole age, and water temperature showed that age explained a large portion of the variance in infection status. Across sites and seasons, larger, older tadpoles had increased mean probabilities of infection, indicating that a large component of the variation among individuals was related to age, and thus to cumulative infection risk. Our results indicate that in systems with annual tadpoles, seasonal changes in infection prevalence may be strongly affected by seasonal patterns of tadpole growth and development in addition to stream type, season, and water temperature. These effects may then influence prevalence of infection in terrestrial individuals in species that have relatively frequent contact with water. This reinforces the need to integrate studies of the drivers of pathogen prevalence across all host life history stages.

Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan

Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011-2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.

epidemix-An interactive multi-model application for teaching and visualizing infectious disease transmission

Mathematical models of disease transmission are used to improve our understanding of patterns of infection and to identify factors influencing them. During recent public and animal health crises, such as pandemic influenza, Ebola, Zika, foot-and-mouth disease, models have made important contributions in addressing policy questions, especially through the assessment of the trajectory and scale of outbreaks, and the evaluation of control interventions. However, their mathematical formulation means that they may appear as a "black box" to those without the appropriate mathematical background. This may lead to a negative perception of their utility for guiding policy, and generate expectations, which are not in line with what these models can deliver. It is therefore important for policymakers, as well as public health and animal health professionals and researchers who collaborate with modelers and use results generated by these models for policy development or research purpose, to understand the key concepts and assumptions underlying these models. The software application epidemix (http://shinyapps.rvc.ac.uk) presented here aims to make mathematical models of disease transmission accessible to a wider audience of users. By developing a visual interface for a suite of eight models, users can develop an understanding of the impact of various modelling assumptions - especially mixing patterns - on the trajectory of an epidemic and the impact of control interventions, without having to directly deal with the complexity of mathematical equations and programming languages. Models are compartmental or individual-based, deterministic or stochastic, and assume homogeneous or heterogeneous-mixing patterns (with the probability of transmission depending on the underlying structure of contact networks, or the spatial distribution of hosts). This application is intended to be used by scientists teaching mathematical modelling short courses to non-specialists - including policy makers, public and animal health professionals and students - and wishing to develop hands-on practicals illustrating key concepts of disease dynamics and control.

Modeling the spatio-temporal dynamics of porcine reproductive & respiratory syndrome cases at farm level using geographical distance and pig trade network matrices

Background

Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT_ji) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight.

Results

The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124).

Conclusion

The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.

Mathematical modelling for Zoonotic Visceral Leishmaniasis dynamics: A new analysis considering updated parameters and notified human Brazilian data

Brazil is one of the highest endemic countries for Zoonotic Visceral Leishmaniasis: according to the Brazilian Ministry of Health, the annual number of new human cases and deaths due to this disease has been increasing for the last 20 years. In addition, regarding the Americas, the specific relationship between canine and human for Visceral Leishmaniasis dynamics is still not well understood. In this work we propose a new model for Zoonotic Visceral Leishmaniasis, based on the models previously published by Burattini et al. (1998) and Ribas et al. (2013). Herein, we modeled the disease dynamics using a modified set of differential equations from those two authors, considering the same assumptions (inclusion of human, dog and sandfly populations, all constants over time). From this set of equations we were able to calculate the basic reproduction number R 0 and to analyze the stability and sensitivity of the system to the parameters variability. As main result, when the stability of the system is reached, the normalized reporting human cases rate is estimated in 9.12E-08/day. This estimation is very close to the 2015 report from Araçatuba city, 5.69E-08/day. We also observed from stability and sensitivity analysis that the activity of sandfly population is critical to introduction and maintenance of Zoonotic Visceral Leishmaniasis in the population. In addition, the importance of dog as source of infection concentrates on latent dog, since it does not show clinical symptoms and signs and, therefore, has a great contribution to disease dissemination. As conclusion, considering the presently ethical issues regarding to elimination of positive dog in Brazil and the highly sensitivity of disease dynamics on sandfly population, we recommend that the sandfly population control should be prioritized.

Urbanization and the dynamics of RNA viruses in Mallards (Anas platyrhynchos)

Urbanization is intensifying worldwide, and affects the epidemiology of infectious diseases. However, the effect of urbanization on natural host-pathogen systems remains poorly understood. Urban ducks occupy an interesting niche in that they directly interact with both humans and wild migratory birds, and either directly or indirectly with food production birds. Here we have collected samples from Mallards (Anas platyrhynchos) residing in a pond in central Uppsala, Sweden, from January 2013 to January 2014. This artificial pond is kept ice-free during the winter months, and is a popular location where the ducks are fed, resulting in a resident population of ducks year-round. Nine hundred and seventy seven (977) fecal samples were screened for RNA viruses including: influenza A virus (IAV), avian paramyxovirus 1, avian coronavirus (CoV), and avian astrovirus (AstroV). This intra-annual dataset illustrates that these RNA viruses exhibit similar annual patterns to IAV, suggesting similar ecological factors are at play. Furthermore, in comparison to wild ducks, autumnal prevalence of IAV and CoV are lower in this urban population. We also demonstrate that AstroV might be a larger burden to urban ducks than IAV, and should be better assessed to demonstrate the degree to which wild birds contribute to the epidemiology of these viruses. The presence of economically relevant viruses in urban Mallards highlights the importance of elucidating the ecology of wildlife pathogens in urban environments, which will become increasingly important for managing disease risks to wildlife, food production animals, and humans.

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Influenza virus, coronavirus, paramyxovirus, astrovirus detected in urban Mallards

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Viruses share intra-annual dynamics, with autumnal prevalence peak

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Avian astrovirus had the highest prevalence in urban Mallards.

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Prevalence of influenza and coronavirus lower in urban versus to migrating Mallard

Host resistance and tolerance of parasitic gut worms depend on resource availability

Resource availability can significantly alter host-parasite dynamics. Abundant food can provide more resources for hosts to resist infections, but also increase host tolerance of infections by reducing competition between hosts and parasites for food. Whether abundant food favors host resistance or tolerance (or both) might depend on the type of resource that the parasite exploits (e.g., host tissue vs. food), which can vary based on the stage of infection. In our study, we evaluated how low and high resource diets affect Cuban tree frog (Osteopilus septentrionalis) resistance and tolerance of a skin-penetrating, gut nematode Aplectana sp. at each stage of the infection. Compared to a low resource diet, a high resource diet enhanced frog resistance to worm penetration and tolerance while worms traveled to the gut. In contrast, a low resource diet increased resistance to establishment of the infection. After the infection established and worms could access food resources in the gut, a high resource diet enhanced host tolerance of parasites. On a high resource diet, parasitized frogs consumed significantly more food than non-parasitized frogs; when food was then restricted, mass of non-parasitized frogs did not change, whereas mass of parasitized frogs decreased significantly. Thus, a high resource diet increased frog tolerance of established worms because frogs could fully compensate for energy lost to the parasites. Our study shows that host-parasite dynamics are influenced by the effect of resource availability on host resistance and tolerance, which depends on when parasites have access to food and the stage of infection.

Understanding the early dynamics of the 2014 porcine epidemic diarrhea virus (PEDV) outbreak in Ontario using the incidence decay and exponential adjustment (IDEA) model

BACKGROUND

The United States swine industry was first confronted with porcine epidemic diarrhea virus (PEDV) in 2013. In young pigs, the virus is highly pathogenic and the associated morbidity and mortality has a significant negative impact on the swine industry. We have applied the IDEA model to better understand the 2014 PEDV outbreak in Ontario, Canada. Using our simple, 2-parameter IDEA model, we have evaluated the early epidemic dynamics of PEDV on Ontario swine farms.

RESULTS

We estimated the best-fit R0 and control parameter (d) for the between farm transmission component of the outbreak by fitting the model to publically available cumulative incidence data. We used maximum likelihood to compare model fit estimates for different combinations of the R0 and d parameters. Using our initial findings from the iterative fitting procedure, we projected the time course of the epidemic using only a subset of the early epidemic data. The IDEA model projections showed excellent agreement with the observed data based on a 7-day generation time estimate. The best-fit estimate for R0 was 1.87 (95% CI: 1.52 - 2.34) and for the control parameter (d) was 0.059 (95% CI: 0.022 - 0.117). Using data from the first three generations of the outbreak, our iterative fitting procedure suggests that R0 and d had stabilized sufficiently to project the time course of the outbreak with reasonable accuracy.

CONCLUSIONS

The emergence and spread of PEDV represents an important agricultural emergency. The virus presents a significant ongoing threat to the Canadian swine industry. Developing an understanding of the important epidemiological characteristics and disease transmission dynamics of a novel pathogen such as PEDV is critical for helping to guide the implementation of effective, efficient, and economically feasible disease control and prevention strategies that are able to help decrease the impact of an outbreak.

Analysis of a waterborne disease model with socioeconomic classes

Waterborne diseases such as cholera continue to pose serious public health problems in the world today. Transmission parameters can vary greatly with socioeconomic class (SEC) and the availability of clean water. We formulate a multi-patch waterborne disease model such that each patch represents a particular SEC with its own water source, allowing individuals to move between SECs. For a 2-SEC model, we investigate the conditions under which each SEC is responsible for driving a cholera outbreak. We determine the effect of SECs on disease transmission dynamics by comparing the basic reproduction number of the 2-SEC model to that of a homogeneous model that does not take SECs into account. We conclude by extending several results of the 2-SEC model to an n-SEC model.

Infection dynamics in frog populations with different histories of decline caused by a deadly disease

Pathogens can drive host population dynamics. Chytridiomycosis is a fungal disease of amphibians that is caused by the fungus Batrachochytrium dendrobatidis (Bd). This pathogen has caused declines and extinctions in some host species whereas other host species coexist with Bd without suffering declines. In the early 1990s, Bd extirpated populations of the endangered common mistfrog, Litoria rheocola, at high-elevation sites, while populations of the species persisted at low-elevation sites. Today, populations have reappeared at many high-elevation sites where they presently co-exist with the fungus. We conducted a capture-mark-recapture (CMR) study of six populations of L. rheocola over 1 year, at high and low elevations. We used multistate CMR models to determine which factors (Bd infection status, site type, and season) influenced rates of frog survival, recapture, infection, and recovery from infection. We observed Bd-induced mortality of individual frogs, but did not find any significant effect of Bd infection on the survival rate of L. rheocola at the population level. Survival and recapture rates depended on site type and season. Infection rate was highest in winter when temperatures were favourable for pathogen growth, and differed among site types. The recovery rate was high (75.7-85.8%) across seasons, and did not differ among site types. The coexistence of L. rheocola with Bd suggests that (1) frog populations are becoming resistant to the fungus, (2) Bd may have evolved lower virulence, or (3) current environmental conditions may be inhibiting outbreaks of the fatal disease.

Space-time modelling of the spread of pancreas disease (PD) within and between Norwegian marine salmonid farms

Infectious diseases are a constant threat to industrialised farming, which is characterised by high densities of farms and farm animals. Several mathematical and statistical models on spatio-temporal dynamics of infectious diseases in various farmed host populations have been developed during the last decades. Here we present a spatio-temporal stochastic model for the spread of a disease between and within aquaculture farms. The spread between farms is divided into several transmission pathways, including (i) distance related spread and (ii) other types of contagious contacts. The within-farm infection dynamics is modelled by a susceptible-infected-recovered (SIR) model. We apply this framework to model the spread of pancreas disease (PD) in salmon farming, using data covering all farms producing salmonids over 9 years in Norway. The motivation for the study was partly to unravel the spatio-temporal dynamics of PD in salmon farming and partly to use the model for scenario simulation of PD control strategies. We find, for example, that within-farm infection dynamics vary with season and we provide estimates of the timing from unobserved infection events to disease outbreaks on farms are detected. The simulations suggest that if a strategy involving culling of infectious cohorts is implemented, the number of detected disease outbreaks per year may be reduced by 57% after the full effect has been reached. We argue that the high detail and coverage of data on salmonid production and disease occurrence should encourage the use of simulation modelling as a means of testing effects of extensive control measures before they are implemented in the salmon farming industry.

Temporal dynamics, diversity, and interplay in three components of the virodiversity of a Mallard population: influenza A virus, avian paramyxovirus and avian coronavirus

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In the autumn of 2011, 3029 samples collected from 144 Mallards.

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A high prevalence of influenza A with 27 different HA/NA subtype combinations.

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A bimodal seasonal prevalence curve, up to 12%, of gammacoronavirus.

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An increased coronavirus prevalence given birds are coinfected with influenza A.

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Low prevalence and diversity of avian paramyxovirus type 1.

Multiple infections, or simultaneous infection of a host with multiple parasites, are the rule rather than the exception. Interactions between co-occurring pathogens in a population may be mutualistic, competitive or facilitative. For some pathogen combinations, these interrelated effects will have epidemiological consequences; however this is as yet poorly incorporated into practical disease ecology. For example, screening of Mallards for influenza A viruses (IAV) have repeatedly revealed high prevalence and large subtype diversity in the Northern Hemisphere. Other studies have identified avian paramyxovirus type 1 (APMV-1) and coronaviruses (CoVs) in Mallards, but without making inferences on the larger viral assemblage. In this study we followed 144 wild Mallards across an autumn season in a natural stopover site and constructed infection histories of IAV, APMV-1 and CoV. There was a high prevalence of IAV, comprising of 27 subtype combinations, while APMV-1 had a comparatively low prevalence (with a peak of 2%) and limited strain variation, similar to previous findings. Avian CoVs were common, with prevalence up to 12%, and sequence analysis identified different putative genetic lineages. An investigation of the dynamics of co-infections revealed a synergistic effect between CoV and IAV, whereby CoV prevalence was higher given that the birds were co-infected with IAV. There were no interactive effects between IAV and APMV-1. Disease dynamics are the result of an interplay between parasites, host immune responses, and resources; and is imperative that we begin to include all factors to better understand infectious disease risk.

Incorporating heterogeneity into the transmission dynamics of a waterborne disease model

We formulate a mathematical model that captures the essential dynamics of waterborne disease transmission to study the effects of heterogeneity on the spread of the disease. The effects of heterogeneity on some important mathematical features of the model such as the basic reproduction number, type reproduction number and final outbreak size are analysed accordingly. We conduct a real-world application of this model by using it to investigate the heterogeneity in transmission in the recent cholera outbreak in Haiti. By evaluating the measure of heterogeneity between the administrative departments in Haiti, we discover a significant difference in the dynamics of the cholera outbreak between the departments.