Indirect interaction between an endemic and an invading pathogen: A case study of Plasmodium and Usutu virus dynamics in a shared bird host population.

Infectious disease agents can influence each other's dynamics in shared host populations. We consider such influence for two mosquito-borne infections where one pathogen is endemic at the time that a second pathogen invades. We regard a setting where the vector has a bias towards biting host individuals infected with the endemic pathogen and where there is a cost to co-infected hosts. As a motivating case study, we regard Plasmodium spp., that cause avian malaria, as the endemic pathogen, and Usutu virus (USUV) as the invading pathogen. Hosts with malaria attract more mosquitoes compared to susceptible hosts, a phenomenon named vector bias. The possible trade-off between the vector-bias effect and the co-infection mortality is studied using a compartmental epidemic model. We focus first on the basic reproduction number R0 for Usutu virus invading into a malaria-endemic population, and then explore the long-term dynamics of both pathogens once Usutu virus has become established. We find that the vector bias facilitates the introduction of malaria into a susceptible population, as well as the introduction of Usutu in a malaria-endemic population. In the long term, however, both a vector bias and co-infection mortality lead to a decrease in the number of individuals infected with either pathogen, suggesting that avian malaria is unlikely to be a promoter of Usutu invasion. This proposed approach is general and allows for new insights into other negative associations between endemic and invading vector-borne pathogens.

Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization.

Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.

Exploring the influence of host community composition on the outbreak potential of Anaplasma phagocytophilum and Borrelia burgdorferi s.l.

In large parts of the northern hemisphere, multiple deer species coexist, and management actions can strongly influence wild deer communities. Such changes may also indirectly influence other species in the community, such as small mammals and birds, because deer can have strong effects on their habitats and resources. Deer, small mammals and birds play an important role in the dynamics of tick-borne zoonotic diseases. It is, however, relatively underexplored how the abundance and composition of vertebrate communities may affect the outbreak potential, maintenance and circulation of tick-borne pathogens. In this study we focus on the outbreak potential by exploring how the basic reproduction number R0 for different tick-borne pathogens depends on host community composition. We used published data on co-varying roe deer (Capreolus capreolus) and fallow deer (Dama dama) densities following a hunting ban, and different small mammal and bird densities, to investigate how the change in host community influences the R0 of four tick-borne pathogens: one non-zoonotic, namely Anaplasma phagocytophilum ecotype 2, and three zoonotic, namely A. phagocytophilum ecotype 1, Borrelia afzelii and Borrelia garinii. We calculated R0 using a next generation matrix approach, and used elasticities to quantify the contributions to R0 of the different groups of host species. The value of R0 for A. phagocytophilum ecotype 1 was higher with high fallow deer density and low roe deer density, while it was the other way round for A. phagocytophilum ecotype 2. For B. afzelii, R0 was mostly related to the density of small mammals and for B. garinii it was mostly determined by bird density. Our results show that the effect of species composition is substantial in the outbreak potential of tick-borne pathogens. This implies that also management actions that change this composition, can (indirectly and unintentionally) affect the outbreak potential of tick-borne diseases.

Estimation of the basic reproduction number for Streptococcus equi spp. equi outbreaks by meta-analysis of strangles outbreak reports.

BACKGROUND: Streptococcus equi spp. equi (S. equi), the cause of strangles in horses, is considered a highly contagious pathogen affecting equines and the equine industry worldwide. Fundamental epidemiological characteristics of outbreaks, such as the basic reproduction number (R0 ), are not well described. OBJECTIVES: Estimate R0 for S. equi in equine populations from outbreak data. STUDY DESIGN: Systematic review and meta-analysis of published and unpublished data. METHODS: A literature search for outbreak reports was carried out. Depending on data available in the reports, the early epidemic growth rate or final attack rate (AR) approach was used to estimate the basic reproduction number for that outbreak. Other recorded outbreak characteristics were the type of housing (group vs. individual). An overall estimate for R0 was computed by meta-analysis. RESULTS: Data from eight outbreaks were extracted from peer-reviewed publications. Data from two additional, non-published outbreaks was also included in the meta-analysis. A conservative estimate for R0 was 2.2 (95% confidence interval [CI] 1.9-2.5). A less conservative estimate, including outbreaks with a 100% AR for which a lower limit R0 was estimated, was 2.7 (95% CI 2.1-3.3). MAIN LIMITATIONS: Few papers describing longitudinal incidence data were found so most estimates were based on the outbreaks' final size. Several outbreaks had a 100% attack rate and could therefore only be included as a lower limit estimate in the meta-analysis. The reported result therefore may be an underestimation. CONCLUSIONS: This estimate for R0 for S. equi informs parameters for future mathematical modelling, quantifies desired preventive vaccine coverage and helps evaluate the effect of prevention strategies through future modelling studies.

Methicillin-resistant staphylococcus aureus nosocomial infection has a distinct epidemiological position and acts as a marker for overall hospital-acquired infection trends.

An ongoing healthcare debate is whether controlling hospital-acquired infection (HAI) from methicillin-resistant Staphylococcus aureus (MRSA) will result in lowering the global HAI rate, or if MRSA will simply be replaced by another pathogen and there will be no change in overall disease burden. With surges in drug-resistant hospital-acquired pathogens during the COVID-19 pandemic, this remains an important issue. Using a dataset of more than 1 million patients in 51 acute care facilities across the USA, and with the aid of a threshold model that models the nonlinearity in outbreaks of diseases, we show that MRSA is additive to the total burden of HAI, with a distinct 'epidemiological position', and does not simply replace other microbes causing HAI. Critically, as MRSA is reduced it is not replaced by another pathogen(s) but rather lowers the overall HAI burden. The analysis also shows that control of MRSA is a benchmark for how well all non-S. aureus nosocomial infections in the same hospital are prevented. Our results are highly relevant to healthcare epidemiologists and policy makers when assessing the impact of MRSA on hospitalized patients. These findings further stress the major importance of MRSA as a unique cause of nosocomial infections, as well as its pivotal role as a biomarker in demonstrating the measured efficacy (or lack thereof) of an organization's Infection Control program.

Modelling the dynamic relationship between spread of infection and observed crowd movement patterns at large scale events.

Understanding how contact patterns arise from crowd movement is crucial for assessing the spread of infection at mass gathering events. Here we study contact patterns from Wi-Fi mobility data of large sports and entertainment events in the Johan Cruijff ArenA stadium in Amsterdam. We show that crowd movement behaviour at mass gathering events is not homogeneous in time, but naturally consists of alternating periods of movement and rest. As a result, contact duration distributions are heavy-tailed, an observation which is not explained by models assuming that pedestrian contacts are analogous to collisions in the kinetic gas model. We investigate the effect of heavy-tailed contact duration patterns on the spread of infection using various random walk models. We show how different types of intermittent movement behaviour interact with a time-dependent infection probability. Our results point to the existence of a crossover point where increased contact duration presents a higher level of transmission risk than increasing the number of contacts. In addition, we show that different types of intermittent movement behaviour give rise to different mass-action kinetics, but also show that neither one of two mass-action mechanisms uniquely describes events.

The role of inter-regional mobility in forecasting SARS-CoV-2 transmission.

In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.

Overlapping timescales obscure early warning signals of the second COVID-19 wave.

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.

Nutritional status and prey energy density govern reproductive success in a small cetacean.

A variety of mammals suppress reproduction when they experience poor physical condition or environmental harshness. In many marine mammal species, reproductive impairment has been correlated to polychlorinated biphenyls (PCBs), the most frequently measured chemical pollutants, while the relative importance of other factors remains understudied. We investigate whether reproductively active females abandon investment in their foetus when conditions are poor, exemplified using an extensively studied cetacean species; the harbour porpoise (Phocoena phocoena). Data on disease, fat and muscle mass and diet obtained from necropsies in The Netherlands were used as proxies of health and nutritional status and related to pregnancy and foetal growth. This was combined with published life history parameters for 16 other areas to correlate to parameters reflecting environmental condition: mean energy density of prey constituting diets (MEDD), cumulative human impact and PCB contamination. Maternal nutritional status had significant effects on foetal size and females in poor health had lower probabilities of being pregnant and generally did not sustain pregnancy throughout gestation. Pregnancy rates across the Northern Hemisphere were best explained by MEDD. We demonstrate the importance of having undisturbed access to prey with high energy densities in determining reproductive success and ultimately population size for small cetaceans.

Polluted porpoises: Generational transfer of organic contaminants in harbour porpoises from the southern North Sea.

Persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexachlorobenzene (HCB), bioaccumulate in marine ecosystems. Top predators contain high levels of POPs in their lipid-rich tissues, which may result in adverse effects on their reproductive, immune and endocrine functions. Harbour porpoises (Phocoena phocoena) are among the smallest of cetaceans and live under high metabolic demand, making them particularly vulnerable to environmental pressures. Using samples from individuals of all maturity classes and sexes stranded along the southern North Sea (n = 121), we show the generational transfer of PCBs, PBDEs and HCB from adults to foetuses. Porpoise placentas contained 1.3-8.2 mg/kg lipid weight (lw) Sum-17PCB, 9 mg/kg lw). This was particularly true for adult males (92.3% >9 mg/kg lw), while adult females had relatively low PCB levels (10.5% >9 mg/kg lw) due to offloading. Nutritional stress led to higher offloading in the milk, causing a greater potential for toxicity in calves of nutritionally stressed females. No correlation between PCB concentration and parasite infestation was detected, although the probability of a porpoise dying due to infectious disease or debilitation increased with increasing PCB concentrations. Despite current regulations to reduce pollution, these results provide further evidence of potential health effects of POPs on harbour porpoises of the southern North Sea, which may consequently increase their susceptibility to other pressures.

Wild ungulate species differ in their contribution to the transmission of Ixodes ricinus-borne pathogens.

BACKGROUND: Several ungulate species are feeding and propagation hosts for the tick Ixodes ricinus as well as hosts to a wide range of zoonotic pathogens. Here, we focus on Anaplasma phagocytophilum and Borrelia burgdorferi (s.l.), two important pathogens for which ungulates are amplifying and dilution hosts, respectively. Ungulate management is one of the main tools to mitigate human health risks associated with these tick-borne pathogens. Across Europe, different species of ungulates are expanding their ranges and increasing in numbers. It is currently unclear if and how the relative contribution to the life-cycle of I. ricinus and the transmission cycles of tick-borne pathogens differ among these species. In this study, we aimed to identify these relative contributions for five European ungulate species. METHODS: We quantified the tick load and collected ticks and spleen samples from hunted fallow deer (Dama dama, n = 131), moose (Alces alces, n = 15), red deer (Cervus elaphus, n = 61), roe deer (Capreolus capreolus, n = 30) and wild boar (Sus scrofa, n = 87) in south-central Sweden. We investigated the presence of tick-borne pathogens in ticks and spleen samples using real-time PCR. We determined if ungulate species differed in tick load (prevalence and intensity) and in infection prevalence in their tissue as well as in the ticks feeding on them. RESULTS: Wild boar hosted fewer adult female ticks than any of the deer species, indicating that deer are more important as propagation hosts. Among the deer species, moose had the lowest number of female ticks, while there was no difference among the other deer species. Given the low number of infected nymphs, the relative contribution of all ungulate species to the transmission of B. burgdorferi (s.l.) was low. Fallow deer, red deer and roe deer contributed more to the transmission of A. phagocytophilum than wild boar. CONCLUSIONS: The ungulate species clearly differed in their role as a propagation host and in the transmission of B. burgdorferi and A. phagocytophilum. This study provides crucial information for ungulate management as a tool to mitigate zoonotic disease risk and argues for adapting management approaches to the local ungulate species composition and the pathogen(s) of concern.

Pressure measurement in the reticulum to detect different behaviors of healthy cows.

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and 'other', as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows.

Trade-offs between mobility restrictions and transmission of SARS-CoV-2.

In their response to the COVID-19 outbreak, governments face the dilemma to balance public health and economy. Mobility plays a central role in this dilemma because the movement of people enables both economic activity and virus spread. We use mobility data in the form of counts of travellers between regions, to extend the often-used SEIR models to include mobility between regions. We quantify the trade-off between mobility and infection spread in terms of a single parameter, to be chosen by policy makers, and propose strategies for restricting mobility so that the restrictions are minimal while the infection spread is effectively limited. We consider restrictions where the country is divided into regions, and study scenarios where mobility is allowed within these regions, and disallowed between them. We propose heuristic methods to approximate optimal choices for these regions. We evaluate the obtained restrictions based on our trade-off. The results show that our methods are especially effective when the infections are highly concentrated, e.g. around a few municipalities, as resulting from superspreading events that play an important role in the spread of COVID-19. We demonstrate our method in the example of the Netherlands. The results apply more broadly when mobility data are available.

The effects of cow introductions on milk production and behaviour of the herd measured with sensors.

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd.

The role of different Culex mosquito species in the transmission of West Nile virus and avian malaria parasites in Mediterranean areas.

Vector-borne diseases, especially those transmitted by mosquitoes, have severe impacts on public health and economy. West Nile virus (WNV) and avian malaria parasites of the genus Plasmodium are mosquito-borne pathogens that may produce severe disease and illness in humans and birds, respectively, and circulate in an endemic form in southern Europe. Here, we used field-collected data to identify the impact of Culex pipiens, Cx. perexiguus and Cx. modestus, on the circulation of both WNV and Plasmodium in Andalusia (SW Spain) using mathematical modelling of the basic reproduction number (R0 ). Models were calibrated with field-collected data on WNV seroprevalence and Plasmodium infection in wild house sparrows, presence of WNV and Plasmodium in mosquito pools, and mosquito blood-feeding patterns. This approach allowed us to determine the contribution of each vector species to pathogen amplification. Overall, 0.7% and 29.6% of house sparrows were positive to WNV antibodies and Plasmodium infection, respectively. In addition, the prevalence of Plasmodium was higher in Cx. pipiens (2.0%), followed by Cx. perexiguus (1.8%) and Cx. modestus (0.7%). Three pools of Cx. perexiguus were positive to WVN. Models identified Cx. perexiguus as the most important species contributing to the amplification of WNV in southern Spain. For Plasmodium models, R0 values were higher when Cx. pipiens was present in the population, either alone or in combination with the other mosquito species. These results suggest that the transmission of these vector-borne pathogens depends on different Culex species, and consequently, their transmission niches will present different spatial and temporal patterns. For WNV, targeted surveillance and control of Cx. perexiguus populations appear as the most effective measure to reduce WNV amplification. Also, preventing Culex populations near human settlements, or reducing the abundance of these species, are potential strategies to reduce WNV spillover into human populations in southern Spain.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Exploring vector-borne infection ecology in multi-host communities: A case study of West Nile virus.

In this study, we develop a model to investigate how ecological factors might affect the dynamics of a vector-borne pathogen in a population composed by different hosts which interact with each other. Specifically, we consider the case when different host species compete with each other, as they share the same habitat, and the vector might have different feeding preference, which can also be time dependent. As a prototypical example, we apply our model to study the invasion and spread, during a typical season, of West Nile virus in an ecosystem composed of two competent avian host species and possibly of dead-end host species. We found that competition and vector feeding preferences can profoundly influence pathogen invasion, influencing its probability to start an epidemic, and influencing transmission rates. Finally, when considering time-dependent feeding preferences, as observed in the field, we noted that the virus circulation could be amplified and that the timing of epidemic peaks could be changed. Our work highlights that ecological interactions between hosts can have a profound influence on the dynamics of the pathogen and that, when modeling vector-borne infections, vector feeding behavior should, for this reason, be carefully evaluated.