Proportion Of Infected Individuals Research Articles

Taking the Pulse of Resilience in Conserving Seagrass Meadows.

Foundational habitats such as seagrasses and coral reefs are at severe risk globally from climate warming. Infectious disease associated with warming events is both a cause of decline and an indicator of stress in both habitats. Since new approaches are needed to detect refugia and design climate-smart networks of marine protected areas, we test the hypothesis that the health of eelgrass (Zostera marina) in temperate ecosystems can serve as a proxy indicative of higher resilience and help pinpoint refugia. Eelgrass meadows worldwide are at risk from environmental stressors, including climate warming and disease. Disease outbreaks of Labyrinthula zosterae are associated with recent, widespread declines in eelgrass meadows throughout the San Juan Islands, Washington, USA. Machine language learning, drone surveys, and molecular diagnostics reveal climate impacts on seagrass wasting disease prevalence (proportion of infected individuals) and severity (proportion of infected leaf area) from San Diego, California, to Alaska. Given that warmer temperatures favor many pathogens such as L. zosterae, we hypothesize that absent or low disease severity in meadows could indicate eelgrass resilience to climate and pathogenic stressors. Regional surveys showed the San Juan Islands as a hotspot for both high disease prevalence and severity, and surveys throughout the Northeast Pacific indicated higher prevalence and severity in intertidal, rather than subtidal, meadows. Further, among sites with eelgrass declines, losses were more pronounced at sites with shallower eelgrass meadows. We suggest that deeper meadows with the lowest disease severity will be refuges from future warming and pathogenic stressors in the Northeast Pacific. Disease monitoring may be a useful conservation approach for marine foundation species, as low or absent disease severity can pinpoint resilient refugia that should be prioritized for future conservation efforts. Even in declining or at-risk habitats, disease surveys can help identify meadows that may contain especially resilient individuals for future restoration efforts. Our approach of using disease as a pulse point for eelgrass resilience to multiple stressors could be applied to other habitats such as coral reefs to inform conservation and management decisions.

Dynamics of simplicial SEIRS epidemic model: global asymptotic stability and neural Lyapunov functions.

The transmission of infectious diseases on a particular network is ubiquitous in the physical world. Here, we investigate the transmission mechanism of infectious diseases with an incubation period using a networked compartment model that contains simplicial interactions, a typical high-order structure. We establish a simplicial SEIRS model and find that the proportion of infected individuals in equilibrium increases due to the many-body connections, regardless of the type of connections used. We analyze the dynamics of the established model, including existence and local asymptotic stability, and highlight differences from existing models. Significantly, we demonstrate global asymptotic stability using the neural Lyapunov function, a machine learning technique, with both numerical simulations and rigorous analytical arguments. We believe that our model owns the potential to provide valuable insights into transmission mechanisms of infectious diseases on high-order network structures, and that our approach and theory of using neural Lyapunov functions to validate model asymptotic stability can significantly advance investigations on complex dynamics of infectious disease.

SIR model with social gatherings

AbstractWe introduce an extension to Kermack and McKendrick’s classic susceptible–infected–recovered (SIR) model in epidemiology, whose underlying mechanism of infection consists of individuals attending randomly generated social gatherings. This gives rise to a system of ordinary differential equations (ODEs) where the force of the infection term depends non-linearly on the proportion of infected individuals. Some specific instances yield models already studied in the literature, to which the present work provides a probabilistic foundation. The basic reproduction number is seen to depend quadratically on the average size of the gatherings, which may be helpful in understanding how restrictions on social gatherings affect the spread of the disease. We rigorously justify our model by showing that the system of ODEs is the mean-field limit of the jump Markov process corresponding to the evolution of the disease in a finite population.

Measuring the efficacy of a vaccine during an epidemic.

The urgency to develop vaccines during the COVID-19 pandemic has resulted in the acceleration of clinical trials. Specifically, a broad spectrum of efficacy levels has been reported for various vaccines based on phase III cohort studies. Our study demonstrates that conducting large cohort phase III clinical trials during the peak of an epidemic leads to a significant underestimation of vaccine efficacy, even in the absence of confounding factors. Furthermore, we find that this underestimation increases with the proportion of infectious individuals in the population during the experiment and the severity of the epidemic, as measured by its basic reproduction number.

DISTRIBUTION PATTERN OF TREMATODES IN A LARGE SETTLEMENT OF THE PONTO-AZOV MOLLUSK LITHOGLYPHUS NATICOIDES (C. PFEIFFER 1828) (GASTROPODA, HYDROBIIDAE) FROM THE UPPER SECTION OF THE UGLICH RESERVOIR (UPPER VOLGA BASIN)

The Ponto-Azov gastropod mollusk Lithoglyphus naticoides (C. Pfeiffer, 1828) naturalized in the Uglich Reservoir relatively recently (in 2013-2015). The main objective of this study is a comparative analysis of the species diversity and structure of trematode communities in different biotopes, occupied by L. naticoides after the initial phase of its spread in the upper section of this water body (downstream the city of Dubna), characterized by a complex hydrological regime. Approximate coordinates of the geographical center of the large settlement of L. naticoides are 56°53ʹN, 37°25ʹE. It is found that by 2019-2021 the average density of L. naticoides in this settlement reached a consistently high value of about 450 ind./m2. Judging by the variability of shell height in adult mature L. naticoides individuals, even within the same settlement, optimum habitat conditions for this mollusk differ significantly in different biotopes. In a comparative aspect, data from two main hydrobiological sampling locations are considered: in the open part of the reservoir and at the mouth of a large tributary - the Dubna River. It is shown that at both locations the species composition of trematodes in L. naticoides is similar and includes, in particular, parthenitae of Apophallus muehlingi (Jägerskiöld, 1899), Apophallus (= Rossicotrema ) donicus (Skrjabin et Lindtrop, 1919), Parasymphylodora markewitschi Kulakowskaja, 1947, Nicolla skrjabini (Iwanitzky, 1928). However, Sanguinicola volgensis (Razin, 1929) parthenitae, dominant in L. naticoides in the open part of the reservoir, is not found in the sample of mollusks from the mouth of the Dubna River. As compared to the sampling site in the open part of the reservoir, at the mouth of the Dubna River the level of infection with parthenitae of other species is significantly lower: N. skrjabini and P. markewitschi - 7 times, Apophallus spp. - 8 times, Xiphidiocercaria sp. - 13 times. The ratio of males to females among mature individuals of L. naticoides turns out to be close to 1:1, which is usual for many freshwater mollusks. In general, the high occurrence (the infection prevalence - the proportion of infected individuals in the host sample P±SE, %) in the open part of the reservoir indicates the presence of prerequisites for serious outbreaks of helminthoses: Xiphidiocercaria sp. (9.16±2.52%), Apophallus spp. (11.45±2.78%), P. markewitschi (7.63±2.32%), N. skrjabini (7.63±2.32%), S. volgensis (63.36±4.21%). At the same time, according to the data obtained, further spread of L. naticoides in some tributaries of reservoirs in the Upper Volga basin may not have such serious parasitological consequences.

Agent-Based Modeling of COVID-19 Transmission in Philippine Classrooms

Onsite classes in the Philippines have been prohibited since March 2020 due to the SARS-CoV-2 which causes the COVID-19. This forced millions of learners to adapt with new modes of instruction that may not be optimal for their learning. In this study, we implemented an agent-based model in Netlogo that followed common classroom layouts to assess the effects of human interactions to virus transmission. Results show that the highest value of cumulative proportion of infected individuals inside the classroom (CPI) is achieved when the total allowable seating capacity in the classroom is increased from 25 to 50%. Also, varying transmission rates between 5 and 20% does not pose any significant effect on CPI. Furthermore, in three of the four seating arrangements, allowing in-class mobility and class rotations can pose significant increases in CPI averaging from 40 to 70%. Results also showed that factors including maximum number of students and number of initially infected individuals, significantly affect the likelihood of infection apart from the seating arrangement itself. To minimize the risk of transmission inside the classroom setup considered, it is vital to control these factors by adhering to mitigation efforts such as increased testing and symptoms checking, limiting the maximum number of students, and redefining breaks and class rotations.

Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro.

In this work we fit an epidemiological model SEIAQR (Susceptible - Exposed - Infectious - Asymptomatic - Quarantined - Removed) to the data of the first COVID-19 outbreak in Rio de Janeiro, Brazil. Particular emphasis is given to the unreported rate, that is, the proportion of infected individuals that is not detected by the health system. The evaluation of the parameters of the model is based on a combination of error-weighted least squares method and appropriate B-splines. The structural and practical identifiability is analyzed to support the feasibility and robustness of the parameters’ estimation. We use the Bootstrap method to quantify the uncertainty of the estimates. For the outbreak of March–July 2020 in Rio de Janeiro, we estimate about 90% of unreported cases, with a 95% confidence interval (85%, 93%).

Incentives, lockdown, and testing: from Thucydides' analysis to the COVID-19 pandemic.

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In particular, we model the interplay between the government and the population as a principal–agent problem with moral hazard, à la Cvitanić et al. (Finance Stoch 22(1):1–37, 2018), while an epidemic is spreading according to dynamics given by compartmental stochastic SIS or SIR models, as proposed respectively by Gray et al. (SIAM J Appl Math 71(3):876–902, 2011) and Tornatore et al. (Phys A Stat Mech Appl 354(15):111–126, 2005). More precisely, to limit the spread of a virus, the population can decrease the transmission rate of the disease by reducing interactions between individuals. However, this effort—which cannot be perfectly monitored by the government—comes at social and monetary cost for the population. To mitigate this cost, and thus encourage the lockdown of the population, the government can put in place an incentive policy, in the form of a tax or subsidy. In addition, the government may also implement a testing policy in order to know more precisely the spread of the epidemic within the country, and to isolate infected individuals. In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax. The government’s optimisation problems then boils down to solving an Hamilton–Jacobi–Bellman equation. Numerical results confirm that if a tax policy is implemented, the population is encouraged to significantly reduce its interactions. If the government also adjusts its testing policy, less effort is required on the population side, individuals can interact almost as usual, and the epidemic is largely contained by the targeted isolation of positively-tested individuals.

Coinfection with a virus constrains within-host infection load but increases transmission potential of a highly virulent fungal plant pathogen.

The trade‐off between within‐host infection rate and transmission to new hosts is predicted to constrain pathogen evolution, and to maintain polymorphism in pathogen populations. Pathogen life‐history stages and their correlations that underpin infection development may change under coinfection with other parasites as they compete for the same limited host resources. Cross‐kingdom interactions are common among pathogens in both natural and cultivated systems, yet their impacts on disease ecology and evolution are rarely studied. The host plant Plantago lanceolata is naturally infected by both Phomopsis subordinaria, a seed killing fungus, as well as Plantago lanceolata latent virus (PlLV) in the Åland Islands, SW Finland. We performed an inoculation assay to test whether coinfection with PlLV affects performance of two P. subordinaria strains, and the correlation between within‐host infection rate and transmission potential. The strains differed in the measured life‐history traits and their correlations. Moreover, we found that under virus coinfection, within‐host infection rate of P. subordinaria was smaller but transmission potential was higher compared to strains under single infection. The negative correlation between within‐host infection rate and transmission potential detected under single infection became positive under coinfection with PlLV. To understand whether within‐host and between‐host dynamics are correlated in wild populations, we surveyed 260 natural populations of P. lanceolata for P. subordinaria infection occurrence. When infections were found, we estimated between‐hosts dynamics by determining pathogen population size as the proportion of infected individuals, and within‐host dynamics by counting the proportion of infected flower stalks in 10 infected plants. In wild populations, the proportion of infected flower stalks was positively associated with pathogen population size. Jointly, our results suggest that the trade‐off between within‐host infection load and transmission may be strain specific, and that the pathogen life‐history that underpin epidemics may change depending on the diversity of infection, generating variation in disease dynamics.

SARS-CoV-2 seroprevalence in the city of Hyderabad, India in early 2021

BackgroundCOVID-19 emerged as a global pandemic in 2020, spreading rapidly to most parts of the world. The proportion of infected individuals in a population can be reliably estimated via serosurveillance, making it a valuable tool for planning control measures. Our serosurvey study aimed to investigate SARS-CoV-2 seroprevalence in the urban population of Hyderabad at the end of the first wave of infections. MethodsThis cross-sectional survey, conducted in January 2021 and including males and females aged 10 years and above, used multi-stage random sampling. 9363 samples were collected from 30 wards distributed over six zones of Hyderabad, and tested for antibodies against SARS-CoV-2 nucleocapsid antigen. ResultsOverall seropositivity was 54.2%, ranging from 50% to 60% in most wards. Highest exposure appeared to be among those aged 30–39 and 50–59 years, with women showing greater seropositivity. Seropositivity increased with family size, with only marginal differences among people with varying levels of education. Seroprevalence was significantly lower among smokers. Only 11% of the survey subjects reported any COVID-19 symptoms, while 17% had appeared for COVID-19 testing. ConclusionOver half the city's population was infected within a year of onset of the pandemic. However, ∼ 46% of people remained susceptible, contributing to subsequent waves of infection.

Interventions to control nosocomial transmission of SARS-CoV-2: a modelling study

BackgroundEmergence of more transmissible SARS-CoV-2 variants requires more efficient control measures to limit nosocomial transmission and maintain healthcare capacities during pandemic waves. Yet the relative importance of different strategies is unknown.MethodsWe developed an agent-based model and compared the impact of personal protective equipment (PPE), screening of healthcare workers (HCWs), contact tracing of symptomatic HCWs and restricting HCWs from working in multiple units (HCW cohorting) on nosocomial SARS-CoV-2 transmission. The model was fit on hospital data from the first wave in the Netherlands (February until August 2020) and assumed that HCWs used 90% effective PPE in COVID-19 wards and self-isolated at home for 7 days immediately upon symptom onset. Intervention effects on the effective reproduction number (RE), HCW absenteeism and the proportion of infected individuals among tested individuals (positivity rate) were estimated for a more transmissible variant.ResultsIntroduction of a variant with 56% higher transmissibility increased — all other variables kept constant — RE from 0.4 to 0.65 (+ 63%) and nosocomial transmissions by 303%, mainly because of more transmissions caused by pre-symptomatic patients and HCWs. Compared to baseline, PPE use in all hospital wards (assuming 90% effectiveness) reduced RE by 85% and absenteeism by 57%. Screening HCWs every 3 days with perfect test sensitivity reduced RE by 67%, yielding a maximum test positivity rate of 5%. Screening HCWs every 3 or 7 days assuming time-varying test sensitivities reduced RE by 9% and 3%, respectively. Contact tracing reduced RE by at least 32% and achieved higher test positivity rates than screening interventions. HCW cohorting reduced RE by 5%. Sensitivity analyses show that our findings do not change significantly for 70% PPE effectiveness. For low PPE effectiveness of 50%, PPE use in all wards is less effective than screening every 3 days with perfect sensitivity but still more effective than all other interventions.ConclusionsIn response to the emergence of more transmissible SARS-CoV-2 variants, PPE use in all hospital wards might still be most effective in preventing nosocomial transmission. Regular screening and contact tracing of HCWs are also effective interventions but critically depend on the sensitivity of the diagnostic test used.

Prevalence of SARS-CoV-2 antibodies in France: results from nationwide serological surveillance

Assessment of the cumulative incidence of SARS-CoV-2 infections is critical for monitoring the course and extent of the COVID-19 epidemic. Here, we report estimated seroprevalence in the French population and the proportion of infected individuals who developed neutralising antibodies at three points throughout the first epidemic wave. Testing 11,000 residual specimens for anti-SARS-CoV-2 IgG and neutralising antibodies, we find nationwide seroprevalence of 0.41% (95% CI: 0.05–0.88) mid-March, 4.14% (95% CI: 3.31–4.99) mid-April and 4.93% (95% CI: 4.02–5.89) mid-May 2020. Approximately 70% of seropositive individuals have detectable neutralising antibodies. Infection fatality rate is 0.84% (95% CI: 0.70–1.03) and increases exponentially with age. These results confirm that the nationwide lockdown substantially curbed transmission and that the vast majority of the French population remained susceptible to SARS-CoV-2 in May 2020. Our study shows the progression of the first epidemic wave and provides a framework to inform the ongoing public health response as viral transmission continues globally.

Population-Based Estimates of SARS-CoV-2 Seroprevalence in Houston, Texas as of September 2020.

BackgroundIn contrast to studies that relied on volunteers or convenience sampling, there are few population-based SARS-CoV-2 seroprevalence investigations and most were conducted early in the pandemic. The health department of the fourth largest city in the U.S. recognized that sound estimates of viral impact were needed to inform decision-making.MethodsAdapting standardized disaster research methodology in September 2020, the city was divided into high and low strata based on RT-PCR positivity rates, and census block groups within each stratum were randomly selected with probability proportional to size, followed by random selection of households within each group. Using two immunoassays, the proportion of infected individuals was estimated for the city, as well as by positivity rate and by sociodemographic and other characteristics. The degree of under ascertainment of seroprevalence was estimated based on RT-PCR positive cases.ResultsSeroprevalence was estimated to be 14% with a near two-fold difference in areas with high (18%) versus low (10%) RT-PCR positivity rates and was four times higher compared to case-based surveillance data.ConclusionsSeroprevalence was higher than previously reported and is greater than that estimated from RT-PCR data. Results will be used to inform public health decisions about testing, outreach, and vaccine rollout.

Cluster of COVID-19 in Northern France: A Retrospective Closed Cohort Study

Background: The Oise department in France has been heavily affected by COVID-19 in early 2020. Methods: Between 30 March and 4 April 2020, we conducted a retrospective closed cohort study among pupils, their parents and siblings, as well as teachers and non-teaching staff of a high-school located in Oise. Participants completed a questionnaire that covered history of fever and/or respiratory symptoms since 13 January 2020 and had blood tested for the presence of anti-SARS-CoV-2 antibodies. The infection attack rate (IAR) was defined as the proportion of participants with confirmed SARS-CoV-2 infection based on antibody detection. Blood samples from two blood donor centres collected between 23 and 27 March 2020 in the Oise department were also tested for presence of anti-SARS-CoV-2 antibodies. Findings: Of the 661 participants (median age: 37 years), 171 participants had anti-SARS-CoV-2 antibodies. The overall IAR was 25.9% (95% confidence interval (CI) = 22.6-29.4), and the infection fatality rate was 0% (one-sided 97.5% CI = 0 - 2.1). Nine of the ten participants hospitalised since mid-January were in the infected group, giving a hospitalisation rate of 5.3% (95% CI = 2.4 –9.8). Anosmia and ageusia had high positive predictive values for SARS-CoV-2 infection (84.7% and 88.1%, respectively). Smokers had a lower IAR compared to non-smokers (7.2% versus 28.0%, P <0.001). The proportion of infected individuals who had no symptoms during the study period was 17.0% (95% CI = 11.2 – 23.4). The proportion of donors with anti-SARS-CoV-2 antibodies in two nearby blood banks of the Oise department was 3.0% (95% CI = 1.1 - 6.4). Interpretation: The relatively low IAR observed in an area where SARS-CoV-2 actively circulated weeks before confinement measures indicates that establishing herd immunity will take time, and that lifting these measures in France will be long and complex. Trial Registration: This study was registered with ClinicalTrials.gov (NCT04325646). Funding Statement: OS lab is funded by Institut Pasteur, ANRS, Sidaction, the Vaccine Research Institute (ANR10-LABX-77), Labex IBEID (ANR-10-LABX-62-IBEID), “TIMTAMDEN” ANR-14-CE14-0029, “CHIKV-ViroImmuno” ANR-14-CE14-0015-01 and the Gilead HIV cure program. LG is supported by the French Ministry of Higher Education, Research and Innovation.ME lab is funded by Institut Pasteur, Labex IBEID (ANR-10-LABX-62-IBEID), Reacting, EU grant Recover, ANR Oh’ticks. HM received core grants from the G5 Institut Pasteur Program, the Milieu Interieur Program (ANR-10-LABX-69-01) and INSERM. C.P. is supported by a fellowship from the Agence Nationale de Recherches sur le Sida et les Hepatites Virales (ANRS). SVDW lab is funded by Institut Pasteur, CNRS, Universite de Paris, Sante publique France, Labex IBEID (ANR-10-LABX-62-IBEID), REACTing, EU grant Recover. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Ethical approval by the Comite de Protection des Personnes Ile de France III. Informed consent was obtained from all participants.

A mathematical model for zoonotic transmission of malaria in the Atlantic Forest: Exploring the effects of variations in vector abundance and acrodendrophily.

Transmission foci of autochthonous malaria caused by Plasmodium vivax-like parasites have frequently been reported in the Atlantic Forest in Southeastern and Southern Brazil. Evidence suggests that malaria is a zoonosis in these areas as human infections by simian Plasmodium species have been detected, and the main vector of malaria in the Atlantic Forest, Anopheles (Kerteszia) cruzii, can blood feed on human and simian hosts. In view of the lack of models that seek to predict the dynamics of zoonotic transmission in this part of the Atlantic Forest, the present study proposes a new deterministic mathematical model that includes a transmission compartment for non-human primates and parameters that take into account vector displacement between the upper and lower forest strata. The effects of variations in the abundance and acrodendrophily of An. cruzii on the prevalence of infected humans in the study area and the basic reproduction number (R0) for malaria were analyzed. The model parameters are based on the literature and fitting of the empirical data. Simulations performed with the model indicate that (1) an increase in the abundance of the vector in relation to the total number of blood-seeking mosquitoes leads to an asymptotic increase in both the proportion of infected individuals at steady state and R0; (2) the proportion of infected humans at steady state is higher when displacement of the vector mosquito between the forest strata increases; and (3) in most scenarios, Plasmodium transmission cannot be sustained only between mosquitoes and humans, which implies that non-human primates play an important role in maintaining the transmission cycle. The proposed model contributes to a better understanding of the dynamics of malaria transmission in the Atlantic Forest.

A model for a vector-borne disease with control based on mosquito repellents: A viability analysis

We investigate a model for a vector-borne disease of susceptible-infected-removed type for the hosts in the presence of control measures and state constraints. The control is based on the use of textiles and household items treated with mosquito repellents which alter the mosquitos' host seeking behaviour. We perform a viability analysis of the model, which amounts to finding a viability kernel, or a set of initial conditions such that the proportion of infected individuals in the controlled system remains below a target infection cap for all future times. The viability kernel is characterised by a variational formulation. It is computed numerically from the sub-zero level-set of a value function that solves a Hamilton-Jacobi-Bellman equation.

A multi‐state occupancy modelling framework for robust estimation of disease prevalence in multi‐tissue disease systems

Abstract Given the public health, economic and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi‐tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues. We developed a multi‐state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained). We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal and genital mucosa of 510 feral swine Sus scrofa during the years 2014–2016 in Florida, USA. The constrained model was better supported by data. PrV prevalence estimates varied among tissues and were higher than the naïve estimates, ranging from to 0.06 (CI: 0.02–0.14) in genital to 0.54 (CI: 0.14 ‐ 0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06–0.18) in nasal to 0.51 (CI: 0.21–0.81) in genital tissue. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue‐specific prevalence and detection probabilities similar to ours. Synthesis and applications. We focused on inferences about pathogen prevalence in multi‐tissue disease systems, dealing with both nondetection and potential dependencies among tissues in infection status. We found strong evidence of variation in both prevalence and detection probabilities among tissues. Our results emphasize the importance of sampling multiple tissues and of applying inference methods that account for imperfect detection in the surveillance of systemic diseases. The multi‐state modelling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and can be used even when the infection status of the pathogen in one tissue may depend on the infection status of the pathogen in other tissue(s).

Humoral response and PCR positivity in patients with COVID-19 in the New York City region, USA: an observational study

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. The proportion of infected individuals who seroconvert is still an open question. In addition, it has been shown in some individuals that viral genome can be detected up to 3 months after symptom resolution. We investigated both seroconversion and PCR positivity in a large cohort of convalescent serum donors in the New York City (NY, USA) region.MethodsIn this observational study, we ran an outreach programme in the New York City area. We recruited participants via the REDCap (Vanderbilt University, Nashville, TN, USA) online survey response. Individuals with confirmed or suspected SARS-CoV-2 infection were screened via PCR for presence of viral genome and via ELISA for presence of anti-SARS-CoV-2 spike antibodies. One-way ANOVA and Fisher's exact test were used to measure the association of age, gender, symptom duration, and days from symptom onset and resolution with positive antibody results.FindingsBetween March 26 and April 10, 2020, we measured SARS-CoV-2 antibody titres in 1343 people. Of the 624 participants with confirmed SARS-CoV-2 infection who had serologies done after 4 weeks, all but three seroconverted to the SARS-CoV-2 spike protein, whereas 269 (37%) of 719 participants with suspected SARS-CoV-2 infection seroconverted. PCR positivity was detected up to 28 days from symptom resolution.InterpretationMost patients with confirmed COVID-19 seroconvert, potentially providing immunity to reinfection. We also report that in a large proportion of individuals, viral genome can be detected via PCR in the upper respiratory tract for weeks after symptom resolution, but it is unclear whether this signal represents infectious virus. Analysis of our large cohort suggests that most patients with mild COVID-19 seroconvert 4 weeks after illness, and raises questions about the use of PCR to clear positive individuals.FundingNone.

Haemosporidian parasites of Neotropical birds: Causes and consequences of infection

Abstract Haemosporidian parasites of the genera Plasmodium, Haemoproteus, and Leucocytozoon are among the best studied parasites of Neotropical birds. Here, we describe variation in haemosporidian prevalence (i.e. the proportion of infected individuals in a sampled population) in Neotropical birds. We review correlates of haemosporidian prevalence (including several avian life-history traits, climate, and season) and the population and evolutionary consequences of infection for Neotropical birds. We find that prevalence varies among avian taxonomic families, genera, and even among species within the same genus, suggesting that prevalence reflects multiple factors, some of which (e.g., avian incubation period) are associated with avian family-level variation in prevalence while others (e.g., avian abundance) are associated with variation in prevalence among closely related host species. We find few correlates of prevalence that have been consistently demonstrated across studies. Prevalence in populations of many Neotropical birds has been estimated by microscopic examination of blood smears, which affords limited opportunity for comparison with data generated by molecular methods. However, at one site in the Brazilian Cerrado for which we have data, prevalence determined by microscopy does not correlate with prevalence determined by molecular methods for Plasmodium, but the 2 data types are positively correlated for Haemoproteus. Haemosporidians have been hypothesized to play a role in avian sexual selection, and we find the prevalence of Haemoproteus to be lower in polygynous species than in socially monogamous species in the Neotropics, confirming a pattern shown previously among Nearctic birds.

A new model for the spread of COVID-19 and the improvement of safety

COVID-19 has been spreading rapidly around the world since December 2019. The main goal of this study is to develop a more effective method for diagnosing and predicting the COVID-19 spread and to evaluate the effectiveness of control measures to reduce and stop the virus spread. To this end, the COVID-19 Decision-Making System (CDMS) was developed to study disease transmission. CDMS divides the population into groups as susceptible, infected, cured and dead. The trends of the people’s number in these groups have deterministic and stochastic components. The deterministic components are described by a differential equations system with parameters determined by the data reported. The stochastic components are represented as an indicator of instability that characterizes the tendency of COVID-19 spread. The simulation experiments have shown a good agreement between the CDMS estimates and the data reported in Russia and Greece. The analysis performed showed that the newly-introduced instability indicator may be the precursor to the pandemic dynamics. In this context, our results showed three potential candidates for a second wave of COVID-19 disease: USA, Russia and Brazil. Although the proportion of infected individuals in countries with high temperatures is lower than in European countries and Russia, temperature and humidity are slowly affecting the effects of the pandemic. Finally, the results presented may contribute to the urgent need to reduce the risks associated with the second wave of the COVID-19, to improve public health intervention and safety measures to be taken by various countries.