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Background Patients admitted to intensive care units are frequently exposed to pathogenic microorganisms present in their environment. Exposure to these microbes may lead to the development of hospital-acquired infections that complicate the illness and may be fatal. Amoeba-associated microorganisms (AAMs) are frequently isolated from hospital water networks and are reported to be associated to cases of community and hospital-acquired pneumonia. Methodology/Principal Findings We used a multiplexed immunofluorescence assay to test for the presence of antibodies against AAMs in sera of intensive care unit (ICU) pneumonia patients and compared to patients at the admission to the ICU (controls). Our results show that some AAMs may be more frequently detected in patients who had hospital-acquired pneumonia than in controls, whereas other AAMs are ubiquitously detected. However, ICU patients seem to exhibit increasing immune response to AAMs when the ICU stay is prolonged. Moreover, concomitant antibodies responses against seven different microorganisms (5 Rhizobiales, Balneatrix alpica, and Mimivirus) were observed in the serum of patients that had a prolonged ICU stay. Conclusions/Significance Our work partially confirms the results of previous studies, which show that ICU patients would be exposed to water amoeba-associated microorganisms, and provides information about the magnitude of AAM infection in ICU patients, especially patients that have a prolonged ICU stay. However, the incidence of this exposure on the development of pneumonia remains to assess.

Aphids are the primary vector of plant viruses. Transient aphids, which probe several plants per day, are considered to be the principal vectors of non-persistently transmitted (NPT) viruses. However, resident aphids, which can complete their life cycle on a single host and are affected by agronomic practices, can transmit NPT viruses as well. Moreover, they can interfere both directly and indirectly with transient aphids, eventually shaping plant disease dynamics. By means of an epidemiological model, originally accounting for ecological principles and agronomic practices, we explore the consequences of fertilization and irrigation, pesticide deployment and roguing of infected plants on the spread of viral diseases in crops. Our results indicate that the spread of NPT viruses can be i) both reduced or increased by fertilization and irrigation, depending on whether the interference is direct or indirect; ii) counter-intuitively increased by pesticide application and iii) reduced by roguing infected plants. We show that a better understanding of vectors’ interactions would enhance our understanding of disease transmission, supporting the development of disease management strategies., Author summary A range of both experimental and theoretical studies show that the behaviour and population dynamics of insects depend strongly upon interactions with other insect species. These interactions have the potential to greatly affect the dynamics of insect-vectored plant disease, as transmission of viruses is intimately dependent on the local density of vectors, as well as how they select and move between potential host plants. Surprisingly, the effects of interaction between vector species on epidemics remains little studied and even worse understood, probably because experimentation is costly and difficult. Here, we present a model which permits us to investigate the effect of interaction between a virus, two vector species and the host plant on the spread of viral disease in crops. In this study, our model is used to explore the consequences of common agronomic practices on epidemics. Our study highlights the importance of exploring vectors’ interactions to enhance the understanding of disease transmission, supporting the development of disease management strategies.

Background Human head lice and body lice have been classified based on phenotypic characteristics, including geographical source, ecotype (preferred egg laying site hair or clothes), shape and color. More recently, genotypic studies have been based on mitochondrial genes, nuclear genes and intergenic spacers. Mitochondrial genetic analysis reclassified lice into three genotypes (A, B and C). However, no previous study has attempted to correlate both genotypic and phenotypic data. Materials and Methods Lice were collected in four African countries: Senegal, Burundi, Rwanda and Ethiopia and were photographed to compare their colors. The Multi-Spacer-Typing (MST) method was used to genotype lice belonging to the worldwide Clade A, allowing a comparison of phenotypic and genotypic data. Results No congruence between louse color and genotype has been identified. Phylogenetic analysis of the spacer PM2, performed including lice from other sources, showed the existence of an African cluster of human lice. However, the analysis of other spacers suggested that lice from different areas are interbreeding. Conclusions We identified two geotypes of Clade A head and body lice including one that is specifically African, that can be either black or grey and can live on the head or in clothing. We also hypothesized that lice from different areas are interbreeding.

Zika virus (ZIKV) and chikungunya virus (CHIKV) were recently introduced into the Americas resulting in significant disease burdens. Understanding their spatial and temporal dynamics at the subnational level is key to informing surveillance and preparedness for future epidemics. We analyzed anonymized line list data on approximately 105,000 Zika virus disease and 412,000 chikungunya fever suspected and laboratory-confirmed cases during the 2014–2017 epidemics. We first determined the week of invasion in each city. Out of 1,122, 288 cities met criteria for epidemic invasion by ZIKV and 338 cities by CHIKV. We analyzed risk factors for invasion using linear and logistic regression models. We also estimated that the geographic origin of both epidemics was located in Barranquilla, north Colombia. We assessed the spatial and temporal invasion dynamics of both viruses to analyze transmission between cities using a suite of (i) gravity models, (ii) Stouffer’s rank models, and (iii) radiation models with two types of distance metrics, geographic distance and travel time between cities. Invasion risk was best captured by a gravity model when accounting for geographic distance and intermediate levels of density dependence; Stouffer’s rank model with geographic distance performed similarly well. Although a few long-distance invasion events occurred at the beginning of the epidemics, an estimated distance power of 1.7 (95% CrI: 1.5–2.0) from the gravity models suggests that spatial spread was primarily driven by short-distance transmission. Similarities between the epidemics were highlighted by jointly fitted models, which were preferred over individual models when the transmission intensity was allowed to vary across arboviruses. However, ZIKV spread considerably faster than CHIKV., Author summary Understanding the spread of infectious diseases across space and time is critical for preparedness, designing interventions, and elucidating mechanisms underlying transmission. We analyzed human case data from over 500,000 reported cases to investigate the spread of the recent Zika virus (ZIKV) and chikungunya virus (CHIKV) epidemics in Colombia. Both viruses were introduced into northern Colombia. We found that gravity models and Stouffer’s rank models best described transmission and that transmission mainly occurred over short distances. Our results highlight similarities and key differences between the ZIKV and CHIKV epidemics in Colombia, which can be used to anticipate future epidemic waves and prioritize cities for active surveillance and targeted interventions.

Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases’ contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset., Author summary Contact tracing is a key tool in the control of infectious diseases. However, to successfully contact and quarantine individuals with traditional methods requires time and is limited by available capacity. As cases rise, limited capacity results in only a fraction of contacts being reached each day before the next set of cases is detected. Here we examine the relationships between increasing case numbers, contact tracing efficiency, and the pathogen reproductive number Rt (the number of cases infected by each case) and how these relationships vary with delays between symptom onset and tracing initiation, and incomplete participation in the testing and tracing process. We found that under conditions found throughout much of the world, where only 20–40% of symptomatic cases are detected, contact tracing can reduce Rt by a maximum of 20%, and this benefit quickly dissolves as contacts needing tracing exceed capacity. Increases in the fraction of symptomatic people being tested and reductions in the time between symptom onset and initiating tracing substantially increase the potential impact of tracing in reducing Rt to 60%, but this benefit is also lost as cases rise and contacts needing tracing exceed capacity to do so. Maintaining excess contact tracing capacity and the ability to shift it to areas most in need can substantially reduce pathogen transmission and limit epidemics.

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems., Author summary For single host pathogens, pathogen genetic data have been transformative for understanding the transmission and control of many diseases, particuarly rapidly evolving RNA viruses. However garnering similar insights where pathogens are multi-host is more challenging, particularly when the evolution of the pathogen is slower and pathogen sampling often heavily biased. This is the case for Mycobacterium bovis, the causative agent of bovine Tuberculosis (bTB) and for which the Eurasian badger plays an as yet poorly understood role in transmission and spread. Here we have developed a computational model that incorporates M. bovis genetic data from cattle only with a highly abstracted model of an unobserved reservoir. Our research shows that a model in which the reservoir does not contribute to pathogen diversity, but is a source of infection in spatially localised areas around each farm, better describes the patterns of outbreaks observed in a population-level sample of a single M. bovis genotype in Northern Ireland over a period of 15 years, compared to models in which either the reservoir has no role, disease spread is spatially extensive, or where they generate considerable diversity on their own. While this reservoir model is not explicitly a model of badgers, its characteristics are consistent with other data that would suggest a reservoir consisting of infected badgers that contribute substantially to cattle infection, but could not maintain disease on their own.

The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48–142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than 1 year. Using an age-structured, deterministic model, we explore potential immunity dynamics using contact data from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalized individuals, a year for hospitalized individuals, and the effective reproduction number after lockdown ends is 1.2 (our worst-case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 387 000 infectious individuals and 125 000 daily new cases; threefold greater than in a scenario with permanent immunity. Our models suggest that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown in June until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 is expected to increase due to the secondary wave. Overall, our analysis presents considerations for policy makers on the longer-term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection as immunity to reinfection is not permanent. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Zika virus (ZIKV) is a mosquito-borne pathogen that recently caused a major epidemic in the Americas. Although the majority of ZIKV infections are asymptomatic, the virus has been associated with birth defects in fetuses and newborns of infected mothers as well as neurological complications in adults. We performed a descriptive analysis on approximately 106,000 suspected and laboratory-confirmed cases of Zika virus disease (ZVD) that were reported during the 2015–2017 epidemic in Colombia. We also analyzed a dataset containing patients with neurological complications and recent febrile illness compatible with ZVD. Females had higher cumulative incidence of ZVD than males. Compared to the general population, cases were more likely to be reported in young adults (20 to 39 years of age). We estimated the cumulative incidence of ZVD in pregnant females at 3,120 reported cases per 100,000 population (95% CI: 3,077–3,164), which was considerably higher than the incidence in both males and non-pregnant females. ZVD cases were reported in all 32 departments. Four-hundred and eighteen patients suffered from ZIKV-associated neurological complications, of which 85% were diagnosed with Guillain-Barré syndrome. The median age of ZIKV cases with neurological complications was 12 years older than that of ZVD cases. ZIKV-associated neurological complications increased with age, and the highest incidence was reported among individuals aged 75 and older. Even though neurological complications and deaths due to ZIKV were rare in this epidemic, better risk communication is needed for people living in or traveling to ZIKV-affected areas.

Japanese encephalitis (JE) is an acute infectious disease caused by the Japanese encephalitis virus (JEV) and is transmitted by mosquitoes. Meteorological conditions are known to play a pivotal role in the spread of JEV. In this study, a zero-inflated generalised additive model and a long short-term memory model were used to assess the relationship between the meteorological factors and population density of Culex tritaeniorhynchus as well as the incidence of JE and to predict the prevalence dynamics of JE, respectively. The incidence of JE in the previous month, the mean air temperature and the average of relative humidity had positive effects on the outbreak risk and intensity. Meanwhile, the density of all mosquito species in livestock sheds (DMSL) only affected the outbreak risk. Moreover, the region-specific prediction model of JE was developed in Chongqing by used the Long Short-Term Memory Neural Network. Our study contributes to a better understanding of the JE dynamics and helps the local government establish precise prevention and control measures.

Outbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one hand pool isolation, where we isolate all individuals that belong to a positive PCR test pool, and on the other hand individual isolation, where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in the STRIDE individual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus., Author summary As pre-symptomatic transmission is an important driver of COVID-19 epidemics (i.e., the virus is transmitted before the infected individual is aware of its infection), contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. For this reason, the use of universal testing, where each individual of the community is tested on a regular basis, has been suggested. However, the large amount of PCR tests that is required to facilitate this approach, remains an important impediment. Therefore, we propose a new universal testing procedure that is feasible with the current testing capacity, where we rely on PCR test pooling of individuals that belong to the same households. We evaluate this universal testing procedure in a fine-grained epidemiological model (i.e., an individual-based model) that covers the Belgian population. Through this evaluation, we assess the procedure’s performance to keep the epidemic under control, while allowing for various contact reductions. We assess the robustness of the model, by analysing different levels of community compliance, and we show that weekly universal testing could prove a successful strategy to control SARS-CoV-2 outbreaks.

The incubation period, or the time from infection to symptom onset of COVID-19 has been usually estimated using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in recalling effort of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used the reported viral load data from multiple countries (Singapore, China, Germany, France, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.23 days (95% CI: 5.17, 5.25), 3.29 days (3.25, 3.37), and 8.22 days (8.02, 8.46), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach especially when impractical to directly observe the infection event.

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof., Author summary The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities.

Human and animal infections with bacteria of the genus Sarcina (family Clostridiaceae) are associated with gastric dilation and emphysematous gastritis. However, the potential roles of sarcinae as commensals or pathogens remain unclear. Here, we investigate a lethal disease of unknown etiology that affects sanctuary chimpanzees (Pan troglodytes verus) in Sierra Leone. The disease, which we have named "epizootic neurologic and gastroenteric syndrome" (ENGS), is characterized by neurologic and gastrointestinal signs and results in death of the animals, even after medical treatment. Using a case-control study design, we show that ENGS is strongly associated with Sarcina infection. The microorganism is distinct from Sarcina ventriculi and other known members of its genus, based on bacterial morphology and growth characteristics. Whole-genome sequencing confirms this distinction and reveals the presence of genetic features that may account for the unusual virulence of the bacterium. Therefore, we propose that this organism be considered the representative of a new species, named "Candidatus Sarcina troglodytae". Our results suggest that a heretofore unrecognized complex of related sarcinae likely exists, some of which may be highly virulent. However, the potential role of "Ca. S. troglodytae" in the etiology of ENGS, alone or in combination with other factors, remains a topic for future research.

Background This study reviews the progress of leprosy elimination in Yunnan, China, over the past 30 years and identifies the challenges for the next stage of the program. Methodology/Principal findings Data were collected from the Leprosy Management Information System in China (LEPMIS). The progress made in the elimination of leprosy between 1990 and 2019 was measured. We defined two time periods, time period 1 (1990–2003) and time period 2 (2004–2019), because multidrug therapy (MDT) was launched for the treatment of leprosy in 1990 and a special fund from the central government was established for leprosy in 2004. During the past 30 years, the number of newly detected leprosy patients in Yunnan has steadily declined. In total, 703 newly detected leprosy patients were reported in 1990, and 353 and 136 cases were reported at the end of 2003 and 2019, respectively. At the end of 1990, 90.7% (117/129) of counties in Yunnan Province were identified as leprosy-endemic counties (>1 case per 100,000 population). By the end of 2003 and 2019, 39.3% (46/117) and 85.5% (100/117) of the leprosy-endemic counties, respectively, had dropped below the elimination threshold. The main challenges are the remaining leprosy-endemic counties, the high rate of cases with a contact history, insufficient early detection, and leprosy cases resulting in physical disability. Conclusions/Significance A multifaceted strategy for leprosy elimination in Yunnan Province has been successfully implemented, and remarkable progress has been made in the elimination of leprosy in this area. The priorities for leprosy elimination in the next stage are securing sustainable support and investment from the government, establishing an effective surveillance system, ensuring prompt early detection, providing treatment with MDT, preventing transmission of M. leprae, preventing disability, providing health education, and preventing recurrence of the epidemic situation of leprosy., Author summary Compared to other provinces in China, Yunnan bears a significant burden of leprosy disease. Multidrug therapy (MDT) was launched for leprosy in 1990, and a special fund for leprosy was established in Yunnan Province by the central government in 2004. During the past 30 years, the number of newly detected leprosy patients has steadily declined, accompanied by an enormous decrease in the number of leprosy-endemic regions. Compared to the 1990–2003 period, the period after the establishment of the special fund from the central government (2004–2019) saw a decrease in the proportion of child cases and patients with grade 2 disability (G2D) and in the duration of delay in diagnosis among newly detected patients, while the proportion of patients with paucibacillary (PB) leprosy, the proportion belonging to minority groups, and the proportion of cases detected early increased significantly. The main challenges are the remaining leprosy-endemic counties, the high rate of cases with a contact history, insufficient early detection, and cases resulting in G2D. To eliminate leprosy in Yunnan Province, a multifaceted strategy should be implemented.

Despite significant progress in reaching some milestones of the United Nations Sustainable Development Goals, neonatal and early infant morbidity and mortality remain high, and maternal health remains suboptimal in many countries. Novel and improved preventative strategies with the potential to benefit pregnant women and their infants are needed, with maternal and neonatal immunization representing effective approaches. Experts from immunology, vaccinology, infectious diseases, clinicians, industry, public health, and vaccine-related social sciences convened at the 5th International Neonatal and Maternal Immunization Symposium (INMIS) in Vancouver, Canada, from 15 to 17 September 2019. We critically evaluated the lessons learned from recent clinical studies, presented cutting-edge scientific progress in maternal and neonatal immunology and vaccine development, and discussed maternal and neonatal immunization in the broader context of infectious disease epidemiology and public health. Focusing on practical aspects of research and implementation, we also discussed the safety, awareness, and perception of maternal immunization as an existing strategy to address the need to improve maternal and neonatal health worldwide. The symposium provided a comprehensive scientific and practical primer as well as an update for all those with an interest in maternal and neonatal infection, immunity, and vaccination. The summary presented here provides an update of the current status of progress in maternal and neonatal immunization., SCOPUS: ar.j, info:eu-repo/semantics/published

Knowledge of novel prokaryotic taxon discovery and nomenclature revisions is of importance to clinical microbiology laboratory practice, infectious disease epidemiology, and studies of microbial pathogenesis. Relative to bacterial isolates derived from human clinical specimens, we present an in-depth summary of novel taxonomic designations and revisions to prokaryotic taxonomy that were published in 2018 and 2019. Included are several changes pertinent to former designations of or within Propionibacterium spp., Corynebacterium spp., Clostridium spp., Mycoplasma spp., Methylobacterium spp., and Enterobacteriaceae. Future efforts to ascertain clinical relevance for many of these changes may be augmented by a document development committee that has been appointed by the Clinical and Laboratory Standards Institute.

Andalusia (Southern Spain) is considered one of the main routes of introduction of bluetongue virus (BTV) into Europe, evidenced by a devastating epidemic caused by BTV-1 in 2007. Understanding the pattern and the drivers of BTV-1 spread in Andalusia is critical for effective detection and control of future epidemics. A long-standing metric for quantifying the behaviour of infectious diseases is the case-reproduction ratio (Rt), defined as the average number of secondary cases arising from a single infected case at time t (for t>0). Here we apply a method using epidemic trees to estimate the between-herd case reproduction ratio directly from epidemic data allowing the spatial and temporal variability in transmission to be described. We then relate this variability to predictors describing the hosts, vectors and the environment to better understand why the epidemic spread more quickly in some regions or periods. The Rt value for the BTV-1 epidemic in Andalusia peaked in July at 4.6, at the start of the epidemic, then decreased to 2.2 by August, dropped below 1 by September (0.8), and by October it had decreased to 0.02. BTV spread was the consequence of both local transmission within established disease foci and BTV expansion to distant new areas (i.e. new foci), which resulted in a high variability in BTV transmission, not only among different areas, but particularly through time, which suggests that general control measures applied at broad spatial scales are unlikely to be effective. This high variability through time was probably due to the impact of temperature on BTV transmission, as evidenced by a reduction in the value of Rt by 0.0041 for every unit increase (day) in the extrinsic incubation period (EIP), which is itself directly dependent on temperature. Moreover, within the range of values at which BTV-1 transmission occurred in Andalusia (20.6°C to 29.5°C) there was a positive correlation between temperature and Rt values, although the relationship was not linear, probably as a result of the complex relationship between temperature and the different parameters affecting BTV transmission. Rt values for BTV-1 in Andalusia fell below the threshold of 1 when temperatures dropped below 21°C, a much higher threshold than that reported in other BTV outbreaks, such as the BTV-8 epidemic in Northern Europe. This divergence may be explained by differences in the adaptation to temperature of the main vectors of the BTV-1 epidemic in Andalusia (Culicoides imicola) compared those of the BTV-8 epidemic in Northern Europe (Culicoides obsoletus). Importantly, we found that BTV transmission (Rt value) increased significantly in areas with higher densities of sheep. Our analysis also established that control of BTV-1 in Andalusia was complicated by the simultaneous establishment of several distant foci at the start of the epidemic, which may have been caused by several independent introductions of infected vectors from the North of Africa. We discuss the implications of these findings for BTV surveillance and control in this region of Europe.

To evaluate and compare the risk of emerging vector-borne diseases (VBDs), a Model for INTegrated RISK assessment, MINTRISK, was developed to assess the introduction risk of VBDs for new regions in an objective, transparent and repeatable manner. MINTRISK is a web-based calculation tool, that provides semi-quantitative risk scores that can be used for prioritization purposes. Input into MINTRISK is entered by answering questions regarding entry, transmission, establishment, spread, persistence and impact of a selected VBD. Answers can be chosen from qualitative answer categories with accompanying quantitative explanation to ensure consistent answering. The quantitative information is subsequently used as input for the model calculations to estimate the risk for each individual step in the model and for the summarizing output values (rate of introduction; epidemic size; overall risk). The risk assessor can indicate his uncertainty on each answer, and this is accounted for by Monte Carlo simulation. MINTRISK was used to assess the risk of four VBDs (African horse sickness, epizootic haemorrhagic disease, Rift Valley fever, and West Nile fever) for the Netherlands with the aim to prioritise these diseases for preparedness. Results indicated that the overall risk estimate was very high for all evaluated diseases but epizootic haemorrhagic disease. Uncertainty intervals were, however, wide limiting the options for ranking of the diseases. Risk profiles of the VBDs differed. Whereas all diseases were estimated to have a very high economic impact once introduced, the estimated introduction rates differed from low for Rift Valley fever and epizootic haemorrhagic disease to moderate for African horse sickness and very high for West Nile fever. Entry of infected mosquitoes on board of aircraft was deemed the most likely route of introduction for West Nile fever into the Netherlands, followed by entry of infected migratory birds.

A key parameter in epidemiological modeling which characterizes the spread of an infectious disease is the generation time, or more generally the distribution of infectiousness as a function of time since infection. There is increasing evidence supporting a prolonged viral shedding window for COVID-19, but the transmissibility in this phase is unclear. Based on this, we develop a generalized Susceptible-Exposed-Infected-Resistant (SEIR) model including an additional compartment of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. Using the incidence and fatality data from different countries, we first show that such an assumption also yields a plausible model in explaining the data observed prior to the easing of the lockdown measures (relaxation). We then test the predictive power of this model for different durations and levels of prolonged infectiousness using the incidence data after the introduction of relaxation in Switzerland, and compare it with a model without the chronically infected population to represent the models conventionally used. We show that in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from those obtained under a model in which prolonged infectiousness is not taken into account. Although the existence of a chronically infected population still remains largely hypothetical, we believe that our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach to better interpret the transmission dynamics of COVID-19., Author summary A key epidemiological variable characterizing the spread of an infectious disease is the generation time, defining the time between successive cases in a chain of transmission. Although there is increasing evidence supporting a prolonged viral shedding window for COVID-19, it is currently unclear to what extent prolonged transmission also occurs. Here we investigate the plausibility of a population of chronically infected individuals who can stay infectious for a longer duration than the reported generation time, but with infectivity reduced to varying degrees. By using the daily case and fatality data from various countries, we show that the existence of a chronically infected population is not a possibility that can be easily rejected from an epidemiological perspective. Moreover, in case of a gradual easing on the lockdown measures, the predictions of the model including the chronically infected population vary considerably from the predictions of the conventional epidemiological models. Although it is not possible to either prove or disprove the existence of a hypothetical population purely by modeling, our results provide tentative evidence to consider a chronically infected population as an alternative modeling approach in assessing the transmission dynamics of COVID-19.

A variety of infectious diseases occur in mainland China every year. Cyclic oscillation is a widespread attribute of most viral human infections. Understanding the outbreak cycle of infectious diseases can be conducive for public health management and disease surveillance. In this study, we collected time-series data for 23 class B notifiable infectious diseases from 2004 to 2020 using public datasets from the National Health Commission of China. Oscillatory properties were explored using power spectrum analysis. We found that the 23 class B diseases from the dataset have obvious oscillatory patterns (seasonal or sporadic), which could be divided into three categories according to their oscillatory power in different frequencies each year. These diseases were found to have different preferred outbreak months and infection selectivity. Diseases that break out in autumn and winter are more selective. Furthermore, we calculated the oscillation power and the average number of infected cases of all 23 diseases in the first eight years (2004 to 2012) and the next eight years (2012 to 2020) since the update of the surveillance system. A strong positive correlation was found between the change of oscillation power and the change in the number of infected cases, which was consistent with the simulation results using a conceptual hybrid model. The establishment of reliable and effective analytical methods contributes to a better understanding of infectious diseases’ oscillation cycle characteristics. Our research has certain guiding significance for the effective prevention and control of class B infectious diseases.

Design, sampling and data interpretation constitute an important challenge for wildlife surveillance of avian influenza viruses (AIV). The aim of this study was to construct a model to improve and enhance identification in both different periods and locations of avian species likely at high risk of contact with AIV in a specific wetland. This study presents an individualbased stochastic model for the Ebre Delta as an example of this appliance. Based on the Monte-Carlo method, the model simulates the dynamics of the spread of AIV among wild birds in a natural park following introduction of an infected bird. Data on wild bird species population, apparent AIV prevalence recorded in wild birds during the period of study, and ecological information on factors such as behaviour, contact rates or patterns of movements of waterfowl were incorporated as inputs of the model. From these inputs, the model predicted those species that would introduce most of AIV in different periods and those species and areas that would be at high risk as a consequence of the spread of these AIV incursions. This method can serve as a complementary tool to previous studies to optimize the allocation of the limited AI surveillance resources in a local complex ecosystem. However, this study indicates that in order to predict the evolution of the spread of AIV at the local scale, there is a need for further research on the identification of host factors involved in the interspecies transmission of AIV

Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here, we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).

Background Melioidosis, an often-fatal infectious disease caused by the environmental Gram-negative bacillus Burkholderia pseudomallei, is endemic in tropical countries. Diabetes mellitus and environmental exposure are important risk factors for melioidosis acquisition. We aim to evaluate the effectiveness of a multifaceted prevention programme for melioidosis in diabetics in northeast Thailand. Methodology/Principal findings From April 2014 to December 2018, we conducted a stepped-wedge cluster-randomized controlled behaviour change trial in 116 primary care units (PCUs) in Ubon Ratchathani province, northeast Thailand. The intervention was a behavioural support group session to help diabetic patients adopt recommended behaviours, including wearing rubber boots and drinking boiled water. We randomly allocated the PCUs to receive the intervention starting in March 2016, 2017 and 2018. All diabetic patients were contacted by phone yearly, and the final follow-up was December 2018. Two primary outcomes were hospital admissions involving infectious diseases and culture-confirmed melioidosis. Of 9,056 diabetics enrolled, 6,544 (72%) received a behavioural support group session. During 38,457 person-years of follow-up, we observed 2,195 (24%) patients having 3,335 hospital admissions involved infectious diseases, 80 (0.8%) melioidosis, and 485 (5%) deaths. In the intention-to-treat analysis, implementation of the intervention was not associated with primary outcomes. In the per-protocol analysis, patients who received a behavioural support group session had lower incidence rates of hospital admissions involving infectious diseases (incidence rate ratio [IRR] 0.89; 95%CI 0.80–0.99, p = 0.03) and of all-cause mortality (IRR 0.54; 95%CI 0.43–0.68, p, Author summary Melioidosis, an infectious disease caused by environmental bacterium Burkholderia pseudomallei, is endemic in tropical countries. Diabetes mellitus is the most important risk factor, and routes of infection include skin inoculation, ingestion and inhalation. Prevention guidelines recommend that residents, rice farmers and visitors should wear protective gear such as rubber boots when in direct contact with soil and environmental water, and consume only boiled or bottled water. Here, we conducted a cluster randomized controlled trial to evaluate effectiveness of a multifaceted prevention programme for melioidosis in diabetic patients in northeast Thailand. We enrolled 9,056 diabetic patients in 2014. We randomly allocated primary care units as the unit of randomization to receive the intervention starting in March 2016, 2017 and 2018. All diabetic patients were contacted by phone yearly. We found that diabetic patients who received a behavioural support group session had lower incidence rates of hospital admissions involving infectious diseases and of all-cause mortality, but not of culture-confirmed melioidosis. In conclusion, clear benefits of this multifaceted prevention programme for melioidosis were not seen. We propose that more compelling invitations for the intervention, modification of or addition to the behaviour change techniques used, and more frequent intervention may be needed.

Background Plague is a re-emerging flea-borne infectious disease of global importance and in recent years, Zambia has periodically experienced increased incidence of outbreaks of this disease. However, there are currently no studies in the country that provide a quantitative assessment of the ability of the disease to spread during these outbreaks. This limits our understanding of the epidemiology of the disease especially for planning and implementing quantifiable and cost-effective control measures. To fill this gap, the basic reproduction number, R0, for bubonic plague was estimated in this study, using data from the 2015 Nyimba district outbreak, in the Eastern province of Zambia. R0 is the average number of secondary infections arising from a single infectious individual during their infectious period in an entirely susceptible population. Methodology/Principal findings Secondary epidemic data for the most recent 2015 Nyimba district bubonic plague outbreak in Zambia was analyzed. R0 was estimated as a function of the average epidemic doubling time based on the initial exponential growth rate of the outbreak and the average infectious period for bubonic plague. R0 was estimated to range between 1.5599 [95% CI: 1.382–1.7378] and 1.9332 [95% CI: 1.6366–2.2297], with average of 1.7465 [95% CI: 1.5093–1.9838]. Further, an SIR deterministic mathematical model was derived for this infection and this estimated R0 to be between 1.4 to 1.5, which was within the range estimated above. Conclusions/Significance This estimated R0 for bubonic plague is an indication that each bubonic plague case can typically give rise to almost two new cases during these outbreaks. This R0 estimate can now be used to quantitatively analyze and plan measurable interventions against future plague outbreaks in Zambia., Author summary Bubonic plague is a historically important and neglected bacterial infectious disease that has claimed more than 170 million lives from multiple devastating epidemics especially during mediaeval and earlier times. Despite a recorded decline in the disease for much of the 21st century, incidence has begun to sharply rise again mainly in developing nations including Zambia which recently recorded a five-fold increase in recorded cases by the turn of the century. For developing and resource strained nations like Zambia, such major public health challenges need to be addressed with the most cost-effective, efficient, and sustainable approaches appropriate to the region. This firstly requires developing a sufficient and comprehensive epidemiological understanding of the disease. This study therefore estimated the basic reproduction number (R0) for bubonic plague which is an estimate of the average number of secondary infections expected to arise from a single infectious individual during their infectious period assuming the entire population is susceptible to the infection. Based on data from the Nyimba district bubonic plague outbreak in Zambia, we estimated R0 for the infection to range between 1.5599 to 1.9332 which shows that each infectious individual with bubonic plague can potentially infect up to two other individuals during their infectious period. We also observed that this R0 estimate was comparable to that previously estimated for large medieval bubonic plague epidemics. Estimating this important epidemiological parameter for bubonic plague greatly enhances existing knowledge of the epidemiology and transmission dynamics of the infection in Zambia and creates prospects for better management of the disease through informed planning.