Outbreak Dynamics Research Articles

Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model.

The transmission rate is a central parameter in mathematical models of infectious disease. Its pivotal role in outbreak dynamics makes estimating the current transmission rate and uncovering its dependence on relevant covariates a core challenge in epidemiological research as well as public health policy evaluation. Here, we develop a method for flexibly inferring a time-varying transmission rate parameter, modeled as a function of covariates and a smooth Gaussian process (GP). The transmission rate model is further embedded in a hierarchy to allow information borrowing across parallel streams of regional incidence data. Crucially, the method makes use of optional vaccination data as a first step toward modeling of endemic infectious diseases. Computational techniques borrowed from the Bayesian spatial analysis literature enable fast and reliable posterior computation. Simulation studies reveal that the method recovers true covariate effects at nominal coverage levels. We analyze data from the COVID-19 pandemic and validate forecast intervals on held-out data. User-friendly software is provided to enable practitioners to easily deploy the method in public healthresearch.

Higher-temperature-adapted dengue virus serotype 2 strain exhibits enhanced virulence in AG129 mouse model.

The factors that drive dengue virus (DENV) evolution, and selection of virulent variants are yet not clear. Higher environmental temperature shortens DENV extrinsic incubation period in mosquitoes, increases human transmission, and plays a critical role in outbreak dynamics. In the present study, we looked at the effect of temperature in altering the virus virulence. We found that DENV cultured at a higher temperature in C6/36 mosquito cells was significantly more virulent than the virus grown at a lower temperature. In a mouse model, the virulent strain induced enhanced viremia and aggressive disease with a short course, hemorrhage, severe vascular permeability, and death. Higher inflammatory cytokine response, thrombocytopenia, and severe histopathological changes in vital organs such as heart, liver, and kidney were hallmarks of the disease. Importantly, it required only a few passages for the virus to acquire a quasi-species population harboring virulence-imparting mutations. Whole genome comparison with a lower temperature passaged strain identified key genomic changes in the structural protein-coding regions as well as in the 3'UTR of the viral genome. Our results point out that virulence-enhancing genetic changes could occur in the dengue virus genome under enhanced growth temperature conditions in mosquito cells.

Dynamics of Zooplankton-Mediated Disease Outbreak in Coral-reef

Dynamics of Zooplankton-Mediated Disease Outbreak in Coral-reef

Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis.

On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot. In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis. Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population. Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.

Numerical Analysis of Fractional-Order Dynamic Dengue Disease Epidemic in Sudan

The main idea of this work is numerical simulation and stability analysis for the fractional-order dynamics of the dengue disease outbreak in Sudan. This research uses a computer technique based on the Adams-Bashforth approach to numerically resolve a fractional-order dengue epidemic in Sudan. Analyses of numerical and dynamic stability show that the fractional-order dengue fever model is sensitive to initial conditions for those parameters. Therefore, the parameters’ values are critical in establishing how many individuals will get better from their sickness and how many will become ill. The proposed method is effective in providing an illustration of the solution’s dynamics over a very long horizon of time, which is crucial for making accurate predictions about the spread of dengue in Sudan. In addition, this method can be utilized to assess the efficacy of various intervention strategies and inform public health policies aimed at reducing the burden of dengue fever in Sudan. It can also assist in identifying areas most susceptible to dengue infestations and prioritizing disease control resources.

Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response

Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

Transmission Dynamics of a Mycobacterium tuberculosis Complex Outbreak in an Indigenous Population in the Colombian Amazon Region.

Whole genome sequencing (WGS) has become the main tool for studying the transmission of Mycobacterium tuberculosis complex (MTBC) strains; however, the clonal expansion of one strain often limits its application in local MTBC outbreaks. The use of an alternative reference genome and the inclusion of repetitive regions in the analysis could potentially increase the resolution, but the added value has not yet been defined. Here, we leveraged short and long WGS read data of a previously reported MTBC outbreak in the Colombian Amazon Region to analyze possible transmission chains among 74 patients in the indigenous setting of Puerto Nariño (March to October 2016). In total, 90.5% (67/74) of the patients were infected with one distinct MTBC strain belonging to lineage 4.3.3. Employing a reference genome from an outbreak strain and highly confident single nucleotide polymorphisms (SNPs) in repetitive genomic regions, e.g., the proline-glutamic acid/proline-proline-glutamic-acid (PE/PPE) gene family, increased the phylogenetic resolution compared to a classical H37Rv reference mapping approach. Specifically, the number of differentiating SNPs increased from 890 to 1,094, which resulted in a more granular transmission network as judged by an increasing number of individual nodes in a maximum parsimony tree, i.e., 5 versus 9 nodes. We also found in 29.9% (20/67) of the outbreak isolates, heterogenous alleles at phylogenetically informative sites, suggesting that these patients are infected with more than one clone. In conclusion, customized SNP calling thresholds and employment of a local reference genome for a mapping approach can improve the phylogenetic resolution in highly clonal MTBC populations and help elucidate within-host MTBC diversity. IMPORTANCE The Colombian Amazon around Puerto Nariño has a high tuberculosis burden with a prevalence of 1,267/100,000 people in 2016. Recently, an outbreak of Mycobacterium tuberculosis complex (MTBC) bacteria among the indigenous populations was identified with classical MTBC genotyping methods. Here, we employed a whole-genome sequencing-based outbreak investigation in order to improve the phylogenetic resolution and gain new insights into the transmission dynamics in this remote Colombian Amazon Region. The inclusion of well-supported single nucleotide polymorphisms in repetitive regions and a de novo-assembled local reference genome provided a more granular picture of the circulating outbreak strain and revealed new transmission chains. Multiple patients from different settlements were possibly infected with at least two different clones in this high-incidence setting. Thus, our results have the potential to improve molecular surveillance studies in other high-burden settings, especially regions with few clonal multidrug-resistant (MDR) MTBC lineages/clades.

Mpox outbreak among men who have sex with men in Amsterdam and Rotterdam, the Netherlands: no evidence for undetected transmission prior to May 2022, a retrospective study.

Since May 2022, over 21,000 mpox cases have been reported from 29 EU/EEA countries, predominantly among men who have sex with men (MSM). The Netherlands was the fourth most affected country in Europe, with more than 1,200 cases and a crude notification rate of 70.7 per million population. The first national case was reported on 10 May, yet potential prior transmission remains unknown. Insight into prolonged undetected transmission can help to understand the current outbreak dynamics and aid future public health interventions. We performed a retrospective study and phylogenetic analysis to elucidate whether undetected transmission of human mpox virus (hMPXV) occurred before the first reported cases in Amsterdam and Rotterdam. In 401 anorectal and ulcer samples from visitors to centres for sexual health in Amsterdam or Rotterdam dating back to 14 February 2022, we identified two new cases, the earliest from 6 May. This coincides with the first cases reported in the United Kingdom, Spain and Portugal. We found no evidence of widespread hMPXV transmission in Dutch sexual networks of MSM before May 2022. Likely, the mpox outbreak expanded across Europe within a short period in the spring of 2022 through an international highly intertwined network of sexually active MSM.

A mathematical model for assessing transient airborne infection risks in a multi-zone hospital ward

Quantification of the parameters influencing airborne infection risks associated with indoor activities in different settings can help in understanding outbreak dynamics and the effective implementation of mitigation strategies. This is particularly important in hospitals, where the consequence of infections amongst vulnerable individuals can be significant. Despite the transient occupancy and inter-connected nature of hospital wards, the majority of airborne risk models assume steady-state conditions and well-mixed air in a single zone. We propose a multi-zone model with both a fixed and time-varying infectious person. We use an adapted version of the Wells–Riley model to estimate transient airborne virus concentration, and account for time-varying behaviour in the indoor setting. Through a coupling with a CONTAM airflow network model, we incorporate the effects of ventilation patterns on inter-zonal flow rates to represent likely airflow in a realistic hospital ward. We use this approach to explore the difference in predictions from transient versus steady-state models across several scenarios. Results suggest that a steady-state approach could lead to an overestimation of infection risk or underestimation of quanta emission, especially when the infectious person is only present for a short period of time. The difference between models is most apparent in poorly ventilated spaces, illustrating how risks can build over time when infectious occupant movement is more frequent than the removal rate due to ventilation. The model highlights the importance of considering transient factors when assessing infection risks to ensure that the most effective mitigation strategies are implemented to address long and short timescale risks.

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States

The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.

The patterns and driving forces of dengue invasions in China

BackgroundGlobal connectivity and environmental change pose continuous threats to dengue invasions from worldwide to China. However, the intrinsic relationship on introduction and outbreak risks of dengue driven by the landscape features are still unknown. This study aimed to map the patterns on source-sink relation of dengue cases and assess the driving forces for dengue invasions in China.MethodsWe identified the local and imported cases (2006–2020) and assembled the datasets on environmental conditions. The vector auto-regression model was applied to detect the cross-relations of source-sink patterns. We selected the major environmental drivers via the Boruta algorithm to assess the driving forces in dengue outbreak dynamics by applying generalized additive models. We reconstructed the internal connections among imported cases, local cases, and external environmental drivers using the structural equation modeling.ResultsFrom 2006 to 2020, 81,652 local dengue cases and 12,701 imported dengue cases in China were reported. The hotspots of dengue introductions and outbreaks were in southeast and southwest China, originating from South and Southeast Asia. Oversea-imported dengue cases, as the Granger-cause, were the initial driver of the dengue dynamic; the suitable local bio-socioecological environment is the fundamental factor for dengue epidemics. The Bio8 [odds ratio (OR) = 2.11, 95% confidence interval (CI): 1.67–2.68], Bio9 (OR = 291.62, 95% CI: 125.63–676.89), Bio15 (OR = 4.15, 95% CI: 3.30–5.24), normalized difference vegetation index in March (OR = 1.27, 95% CI: 1.06–1.51) and July (OR = 1.04, 95% CI: 1.00–1.07), and the imported cases are the major drivers of dengue local transmissions (OR = 4.79, 95% CI: 4.34–5.28). The intermediary effect of an index on population and economic development to local cases via the path of imported cases was detected in the dengue dynamic system.ConclusionsDengue outbreaks in China are triggered by introductions of imported cases and boosted by landscape features and connectivity. Our research will contribute to developing nature-based solutions for dengue surveillance, mitigation, and control from a socio-ecological perspective based on invasion ecology theories to control and prevent future dengue invasion and localization.Graphical

Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak.

Compartmental models are commonly used in practice to investigate the dynamical response of infectious diseases such as the COVID-19 outbreak. Such models generally assume exponentially distributed latency and infectiousness periods. However, the exponential distribution assumption fails when the sojourn times are expected to distribute around their means. This study aims to derive a novel S (Susceptible)-E (Exposed)-P (Presymptomatic)-A (Asymptomatic)-D (Symptomatic)-C (Reported) model with arbitrarily distributed latency, presymptomatic infectiousness, asymptomatic infectiousness, and symptomatic infectiousness periods. The SEPADC model is represented by nonlinear Volterra integral equations that generalize ordinary differential equation-based models. Our primary aim is the derivation of a general relation between intrinsic growth rate r and basic reproduction number with the help of the well-known Lotka-Euler equation. The resulting equation includes separate roles of various stages of the infection and their sojourn time distributions. We show that estimates are considerably affected by the choice of the sojourn time distributions for relatively higher values of r. The well-known exponential distribution assumption has led to the underestimation of values for most of the countries. Exponential and delta-distributed sojourn times have been shown to yield lower and upper bounds of the values depending on the r values. In quantitative experiments, values of 152 countries around the world were estimated through our novel formulae utilizing the parameter values and sojourn time distributions of the COVID-19 pandemic. The global convergence, , has been estimated through our novel formulation. Additionally, we have shown that increasing the shape parameter of the Erlang distributed sojourn times increases the skewness of the epidemic curves in entire dynamics.

The epidemiology and evolutionary dynamics of massive dengue outbreak in China, 2019.

In 2019, China experienced massive dengue outbreaks with high incidence and expanded outbreak areas. The study aims to depict dengue's epidemiology and evolutionary dynamics in China and explore the possible origin of these outbreaks. Records of confirmed dengue cases in 2019 were obtained from the China Notifiable Disease Surveillance System. The sequences of complete envelope gene detected from the outbreak provinces in China in 2019 were retrieved from GenBank. Maximum Likelihood trees were constructed to genotype the viruses. The median-joining network was used to visualize fine-scale genetic relationships. Four methods were used to estimate the selective pressure. A total of 22,688 dengue cases were reported, 71.4% of which were indigenous cases and 28.6% were imported cases (including from abroad and from other domestic provinces). The abroad cases were predominantly imported from Southeast Asia countries (94.6%), with Cambodia (3,234 cases, 58.9%), and Myanmar (1,097 cases, 20.0%) ranked as the top two. A total of 11 provinces with dengue outbreaks were identified in the central-south of China, of which Yunnan and Guangdong provinces had the highest number of imported and indigenous cases. The primary source of imported cases in Yunnan was from Myanmar, while in the other ten provinces, the majority of imported cases were from Cambodia. Guangdong, Yunnan and Guangxi provinces were China's primary sources of domestically imported cases. Phylogenetic analysis of the viruses in outbreak provinces revealed three genotypes: (I, IV, and V) in DENV 1, Cosmopolitan and Asian I genotypes in DENV 2, and two genotypes (I and III) in DENV 3. Some genotypes concurrently circulated in different outbreak provinces. Most of the viruses were clustered with those from Southeast Asia. Haplotype network analysis showed that Southeast Asia, possibly Cambodia and Thailand, was the respective origin of the viruses in clade 1 and 4 for DENV 1. Positive selection was detected at codon 386 in clade 1. Dengue importation from abroad, especially from Southeast Asia, resulted in the dengue epidemic in China in 2019. Domestic transmission between provinces and positive selection on virus evolution may contribute to the massive dengue outbreaks.

Gambierone and Sodium Channel Specific Bioactivity Are Associated with the Extracellular Metabolite Pool of the Marine Dinoflagellate Coolia palmyrensis.

Tropical epibenthic dinoflagellate communities produce a plethora of bioactive secondary metabolites, including the toxins ciguatoxins (CTXs) and potentially gambierones, that can contaminate fishes, leading to ciguatera poisoning (CP) when consumed by humans. Many studies have assessed the cellular toxicity of causative dinoflagellate species to better understand the dynamics of CP outbreaks. However, few studies have explored extracellular toxin pools which may also enter the food web, including through alternative and unanticipated routes of exposure. Additionally, the extracellular exhibition of toxins would suggest an ecological function and may prove important to the ecology of the CP-associated dinoflagellate species. In this study, semi-purified extracts obtained from the media of a Coolia palmyrensis strain (DISL57) isolated from the U.S. Virgin Islands were assessed for bioactivity via a sodium channel specific mouse neuroblastoma cell viability assay and associated metabolites evaluated by targeted and non-targeted liquid chromatography tandem and high-resolution mass spectrometry. We found that extracts of C. palmyrensis media exhibit both veratrine enhancing bioactivity and non-specific bioactivity. LC-HR-MS analysis of the same extract fractions identified gambierone and multiple undescribed peaks with mass spectral characteristics suggestive of structural similarities to polyether compounds. These findings implicate C. palmyrensis as a potential contributor to CP and highlight extracellular toxin pools as a potentially significant source of toxins that may enter the food web through multiple exposure pathways.

SIR-SI model with a Gaussian transmission rate: Understanding the dynamics of dengue outbreaks in Lima, Peru.

Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology. We used the weekly distribution of dengue cases in the Comas, Lurigancho, and Puente Piedra districts, as well as the temperature data to investigate the transmission dynamics. We used maximum likelihood minimization and the human susceptible-infected-recovered and vector susceptible-infected (SIR-SI) model with a Gaussian function for the infectious rate to consider external non-modeled variables. We found that the adjusted SIR-SI model with the Gaussian transmission rate (for modelling the exogenous variables) captured the behavior of the dengue outbreak in the selected districts. The model explained that the transmission behavior had a strong dependence on the weather, cultural, and demographic variables while other variables determined the start of the outbreak. The experimental results showed good agreement with the data and model results when a Bayesian-Gaussian transmission rate was employed. The effect of weather was also observed, and a strong qualitative relationship was obtained between the transmission rate and computed effective reproduction number Rt.

Brucella abortus in Kazakhstan, population structure and comparison with worldwide genetic diversity.

Brucella abortus is the main causative agent of brucellosis in cattle, leading to severe economic consequences in agriculture and affecting public health. The zoonotic nature of the infection increases the need to control the spread and dynamics of outbreaks in animals with the incorporation of high resolution genotyping techniques. Based on such methods, B. abortus is currently divided into three clades, A, B, and C. The latter includes subclades C1 and C2. This study presents the results of whole-genome sequencing of 49 B. abortus strains isolated in Kazakhstan between 1947 and 2015 and of 36 B. abortus strains of various geographic origins isolated from 1940 to 2004. In silico Multiple Locus Sequence Typing (MLST) allowed to assign strains from Kazakhstan to subclades C1 and to a much lower extend C2. Whole-genome Single-Nucleotide Polymorphism (wgSNP) analysis of the 46 strains of subclade C1 with strains of worldwide origins showed clustering with strains from neighboring countries, mostly North Caucasia, Western Russia, but also Siberia, China, and Mongolia. One of the three Kazakhstan strains assigned to subclade C2 matched the B. abortus S19 vaccine strain used in cattle, the other two were genetically close to the 104 M vaccine strain. Bayesian phylodynamic analysis dated the introduction of B. abortus subclade C1 into Kazakhstan to the 19th and early 20th centuries. We discuss this observation in view of the history of population migrations from Russia to the Kazakhstan steppes.

The effect of environmental changes on locust outbreak dynamics in the downstream area of the Yellow River during the Ming and Qing Dynasties

Locust outbreaks were one of the primary biological disasters in ancient China. Using historical data from the Ming and Qing Dynasties, the temporal and spatial relationships between the changes in the aquatic environment and the locust dynamics in the downstream areas of the Yellow River were investigated via quantitative statistics, and other factors affecting locust outbreaks were also studied. This study demonstrated that locust, drought and flood outbreaks were spatiotemporally correlated. Locusts and droughts were synchronous for long-term series, but locust outbreaks were weakly correlated with floods. In drought years, the probability of a locust outbreak occurring in the same month as a drought was higher than that in other years and months. The probability of a locust outbreak was higher in the one to two years following a flood than in other years, but locusts were not easily triggered by extreme flooding. In the waterlogged and riverine locust breeding areas, locust outbreaks were more closely related to flooding and drought than in other breeding areas. Affected by the diversion of the Yellow River, the areas of frequent locust outbreaks were around riverine areas. In addition, climate change affects the hydrothermal conditions in which locusts occur, and human activities influence the occurrence of locusts by changing their habitats. Analyzing the relationship between historical locust outbreaks and water system changes provides valuable information for formulating and implementing disaster prevention and mitigation policies in this region.

The use of path analysis to determine effects of environmental factors on the adult seasonality ofCulicoides(Diptera: Ceratopogonidae) vector species in Spain

Culicoides biting midges (Diptera: Ceratopogonidae) are the main vectors of livestock diseases such as bluetongue (BT) which mainly affect sheep and cattle. In Spain, bluetongue virus (BTV) is transmitted by several Culicoides taxa, including Culicoides imicola, Obsoletus complex, Culicoides newsteadi and Culicoides pulicaris that vary in seasonality and distribution, affecting the distribution and dynamics of BT outbreaks. Path analysis is useful for separating direct and indirect, biotic and abiotic determinants of species' population performance and is ideal for understanding the sensitivity of adult Culicoides dynamics to multiple environmental drivers. Start, end of season and length of overwintering of adult Culicoides were analysed across 329 sites in Spain sampled from 2005 to 2010 during the National Entomosurveillance Program for BTV with path analysis, to determine the direct and indirect effects of land use, climate and host factor variables. Culicoides taxa had species-specific responses to environmental variables. While the seasonality of adult C. imicola was strongly affected by topography, temperature, cover of agro-forestry and sclerophyllous vegetation, rainfall, livestock density, photoperiod in autumn and the abundance of Culicoides females, Obsoletus complex species seasonality was affected by land-use variables such as cover of natural grassland and broad-leaved forest. Culicoides female abundance was the most explanatory variable for the seasonality of C. newsteadi, while C. pulicaris showed that temperature during winter and the photoperiod in November had a strong effect on the start of the season and the length of overwinter period of this species. These results indicate that the seasonal vector-free period (SVFP) in Spain will vary between competent vector taxa and geographic locations, dependent on the different responses of each taxa to environmental conditions.

Model misspecification misleads inference of the spatial dynamics of disease outbreaks

Epidemiology has been transformed by the advent of Bayesian phylodynamic models that allow researchers to infer the geographic history of pathogen dispersal over a set of discrete geographic areas [1, 2]. These models provide powerful tools for understanding the spatial dynamics of disease outbreaks, but contain many parameters that are inferred from minimal geographic information (i.e., the single area in which each pathogen was sampled). Consequently, inferences under these models are inherently sensitive to our prior assumptions about the model parameters. Here, we demonstrate that the default priors used in empirical phylodynamic studies make strong and biologically unrealistic assumptions about the underlying geographic process. We provide empirical evidence that these unrealistic priors strongly (and adversely) impact commonly reported aspects of epidemiological studies, including: 1) the relative rates of dispersal between areas; 2) the importance of dispersal routes for the spread of pathogens among areas; 3) the number of dispersal events between areas, and; 4) the ancestral area in which a given outbreak originated. We offer strategies to avoid these problems, and develop tools to help researchers specify more biologically reasonable prior models that will realize the full potential of phylodynamic methods to elucidate pathogen biology and, ultimately, inform surveillance and monitoring policies to mitigate the impacts of disease outbreaks.