Estimates Of Infection Prevalence Research Articles

The Use of Wastewater Surveillance to Estimate SARS-CoV-2 Fecal Viral Shedding Pattern and Identify Time Periods with Intensified Transmission.

Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants. We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves. We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 ( N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission. Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ∼20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence ( P < 0.05; bootstrapping test). These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

Bayesian evaluation of meat juice ELISA for detecting Salmonella in slaughtered pigs without specifying a cut-off.

Consumption of pork and pork products is a major source of human infection with Salmonella. Salmonella is typically subclinical in pigs, making it difficult to identify infected pigs. Therefore, effective surveillance of Salmonella in pigs critically relies on good knowledge on how well the diagnostic tests used perform. A test that has been used in several countries for Salmonella monitoring is serological testing of meat juice using an ELISA (MJ ELISA) to detect antibodies against Salmonella. This MJ ELISA data could be used to estimate infection prevalence and trends. However, as the MJ ELISA output is a sample-to-positive (S/P) ratio, which is a continuous outcome rather than a binary (positive/negative) result, the interpretation of this data depends upon a chosen cut-off. To apply Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of the MJ ELISA test values in the absence of a gold standard without needing to apply a cut-off. BLCMs were fitted to data from a UK abattoir survey carried out in 2006 in order to estimate the diagnostic accuracy of MJ ELISA with respect to the prevalence of active Salmonella infection. This survey consisted of a MJ ELISA applied in parallel with the bacteriological testing of caecal contents, carcass swabs and lymph nodes (n = 625). A BLCM was also fitted to the same data but with dichotomisation of the MJ ELISA results, in order to compare with the model using continuous outcomes. Estimates were obtained for sensitivity and specificity of the ELISA over a range of S/P values and for the bacteriological tests and were found to be similar between the models using continuous and dichotomous ELISA outcomes. The Bayesian method without specifying a cut-off does allow prevalence to be inferred without specifying a cut-off for the ELISA. The study results will be useful for estimating infection prevalence from serological surveillance data.

A non-invasive feather-based methodology for the detection of blood parasites (Haemosporida)

Blood parasite (haemosporidian) infections are conventionally detected using blood samples; this implies capturing and handling birds to obtain them, which induces stress and causes pain. Feathers have blood vessels, and some blood could be preserved in the feather’s shaft after moulting. We used feather DNA for detecting haemosporidians by PCR testing in diverse scenarios. First, haemosporidian DNA was detected in feathers from carcasses of infected birds, proving the feasibility of the approach. Storage temperature affected DNA recovery, with maximum retrieval and haemosporidian detection at the lowest temperature (− 20 °C). All feather types from infected birds kept at optimal conditions yielded haemosporidian DNA. Parasite detection by PCR was correlated with DNA yield, which was significantly higher in heavier birds, flight feathers, and more feathers per pool. Lastly, haemosporidians were detected employing feathers moulted from wild and captive birds to estimate infection prevalence. We show for the first time that using blood from feather shafts for haemosporidian detection can be an advantageous and less invasive alternative to blood sampling if feathers are optimally preserved. This method could contribute to uncovering haemosporidian infections in endangered and elusive birds, and it might facilitate routine screening in captive birds, thereby improving infection detection, prevention, and control.

Comparison of acarological risk metrics derived from active and passive surveillance and their concordance with tick-borne disease incidence

Tick-borne diseases continue to threaten human health across the United States. Both active and passive tick surveillance can complement human case surveillance, providing spatio-temporal information on when and where humans are at risk for encounters with ticks and tick-borne pathogens. However, little work has been done to assess the concordance of the acarological risk metrics from each surveillance method. We used data on Ixodes scapularis and its associated human pathogens from Connecticut (2019–2021) collected through active collections (drag sampling) or passive submissions from the public to compare county estimates of tick and pathogen presence, infection prevalence, and tick abundance by life stage. Between the surveillance strategies, we found complete agreement in estimates of tick and pathogen presence, high concordance in infection prevalence estimates for Anaplasma phagocytophilum, Borrelia burgdorferi sensu stricto, and Babesia microti, but no consistent relationships between actively and passively derived estimates of tick abundance or abundance of infected ticks by life stage. We also compared nymphal metrics (i.e., pathogen prevalence in nymphs, nymphal abundance, and abundance of infected nymphs) with reported incidence of Lyme disease, anaplasmosis, and babesiosis, but did not find any consistent relationships with any of these metrics. The small spatial and temporal scale for which we had consistently collected active and passive data limited our ability to find significant relationships. Findings are likely to differ if examined across a broader spatial or temporal coverage with greater variation in acarological and epidemiological outcomes. Our results indicate similar outcomes between some actively and passively derived tick surveillance metrics (tick and pathogen presence, pathogen prevalence), but comparisons were variable for abundance estimates.

Deep data mining reveals variable abundance and distribution of microbial reproductive manipulators within and among diverse host species.

Bacterial symbionts that manipulate the reproduction of their hosts are important factors in invertebrate ecology and evolution, and are being leveraged for host biological control. Infection prevalence restricts which biological control strategies are possible and is thought to be strongly influenced by the density of symbiont infection within hosts, termed titer. Current methods to estimate infection prevalence and symbiont titers are low-throughput, biased towards sampling infected species, and rarely measure titer. Here we develop a data mining approach to estimate symbiont infection frequencies within host species and titers within host tissues. We applied this approach to screen ~32,000 publicly available sequence samples from the most common symbiont host taxa, discovering 2,083 arthropod and 119 nematode infected samples. From these data, we estimated that Wolbachia infects approximately 44% of all arthropod and 34% of all nematode species, while other reproductive manipulators only infect 1-8% of arthropod and nematode species. Although relative titers within hosts were highly variable within and between arthropod species, a combination of arthropod host species and Wolbachia strain explained approximately 36% of variation in Wolbachia titer across the dataset. To explore potential mechanisms for host control of symbiont titer, we leveraged population genomic data from the model system Drosophila melanogaster. In this host, we found a number of SNPs associated with titer in candidate genes potentially relevant to host interactions with Wolbachia. Our study demonstrates that data mining is a powerful tool to detect bacterial infections and quantify infection intensities, thus opening an array of previously inaccessible data for further analysis in host-symbiont evolution.

Surveillance for Ixodes scapularis and Ixodes pacificus ticks and their associated pathogens in Canada, 2020.

Ixodes scapularis and Ixodes pacificus ticks are the principal vectors of the agent of Lyme disease and several other tick-borne diseases in Canada. Tick surveillance data can be used to identify local tick-borne disease risk areas and direct public health interventions. The objective of this article is to describe the seasonal and spatial characteristics of the main Lyme disease vectors in Canada, and the tick-borne pathogens they carry, using passive and active surveillance data from 2020. Passive and active surveillance data were compiled from the National Microbiology Laboratory Branch (Public Health Agency of Canada), provincial and local public health authorities, and eTick (an online, image-based platform). Seasonal and spatial analyses of ticks and their associated pathogens are presented, including infection prevalence estimates. In passive surveillance, I. scapularis (n=7,534) were submitted from all provinces except Manitoba and British Columbia, while I. pacificus (n=718) were submitted only from British Columbia. No ticks were submitted from the Territories. The seasonal distribution of I. scapularis submissions was bimodal, but unimodal for I. pacificus. Four tick-borne pathogens were identified in I. scapularis (Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti and Borrelia miyamotoi) and one in I. pacificus (B. miyamotoi). In active surveillance, I. scapularis (n=688) were collected in Ontario, Québec and New Brunswick. Five tick-borne pathogens were identified: B. burgdorferi, A. phagocytophilum, B. microti, B. miyamotoi and Powassan virus. This article provides a snapshot of the distribution of I. scapularis and I. pacificus and their associated human pathogens in Canada in 2020, which can help assess the risk of exposure to tick-borne pathogens in different provinces.

Accounting for bias in prevalence estimation: The case of a globally emerging pathogen

Abstract Accurate quantification of infection parameters is necessary to ensure effective surveillance, investigation and mitigation of infectious diseases. However, hosts and pathogens are often imperfectly observed and key epidemiological parameters, such as infection prevalence, can be biased if this observational uncertainty is not properly accounted for. Here, we evaluated the combined effects of imperfect pathogen detection and host pseudoreplication on the estimation of infection prevalence of the pathogen Batrachochytrium dendrobatidis (Bd) in the southern Darwin's frog (Rhinoderma darwinii). This pathogen causes amphibian chytridiomycosis, a panzootic disease responsible for the greatest documented loss of biodiversity due to an infectious disease. From November 2018 to March 2019, we made 1085 captures of 641 R. darwinii individuals in two areas of Southern Chile. Captured frogs were individually identified to eliminate host pseudoreplication, skin‐swabbed twice in sequence, and each swab was analysed in duplicate using a real‐time polymerase chain reaction (qPCR) assay to detect Bd. To provide a robust estimate of period prevalence, we used a Bayesian multiscale occupancy model that considers pathogen imperfect detection arising from both sampling and diagnostic testing processes. Finally, using a deterministic matrix population model, we illustrated how the method chosen to estimate prevalence influenced our conclusions regarding the impact of Bd infection on host population trajectories. Our results showed that Bd prevalence could be underestimated by 55% if false negatives and host pseudoreplication were not accounted for. Host pseudoreplication had a greater impact on prevalence underestimation than pathogen imperfect detection in our study. This underestimation in prevalence changed our interpretation of the impacts of Bd infections on our model species, from a nearly stable population using the naïve period prevalence to a declining one using our robust estimate. Synthesis and applications. These results highlight the importance of using robust inferences to inform disease risk assessments and to efficiently allocate limited resources during mitigation strategies of infectious diseases. The methods used here can be applied to a wide range of host–pathogen systems, and will be of interest to both researchers and practitioners aiming to investigate and mitigate the impacts of infectious diseases on free‐ranging populations.

Pre-Omicron seroprevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies among a cohort of children and teenagers in Montréal, Canada

To use serological testing to assess the pre-Omicron seroprevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies in children and adolescents in Montreal, Canada. This analysis is from a prospective cohort study of children aged 2 to 17 years (at baseline) that included blood spots (DBS) for antibody detection. The serostatus of participants was determined by enzyme-linked immunosorbent assays (ELISAs) using the receptor-binding domain (RBD) from the spike protein and the nucleocapsid protein (N) as antigens. We estimated seroprevalence, seroconversion rates, and the likelihood of seroreversion at six months and one year. The baseline (October 2020 to April 2021) seroprevalence was 5.8% (95% CI 4.8-7.1), which increased to 10.5% (May to September 2021) and 11.0% (November 2021 to March 2022) for the respective follow-ups (95% CI 8.6-12.7; 95% CI 8.8-13.5). The crude rate of seroconversion over the study period was 12.8 per 100 person-years (95% CI 11.0-14.7). Adjusted hazard rates of seroconversion by child characteristics showed higher rates in children who were female, whose parent identified as a racial or ethnic minority, and in households with incomes in the lowest tercile of our study population. The likelihood of remaining seropositive at six months was 68% (95% CI 60%-77%) and dropped to 42% (95% CI 32%-56%) at one year. Serological studies continue to provide valuable contributions for infection prevalence estimates and help us better understand the dynamics of antibody levels following infection.

Know what you don't know: Embracing state uncertainty in disease‐structured multistate models

Abstract Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark–recapture and dynamic multistate occupancy models are HMMs frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity. However, state uncertainty is a pervasive feature of many ecological studies, and multievent models were developed to explicitly account for it. We developed a novel extended multievent mark–recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease‐structured example, both false negative and false positive state assignment errors are modelled at two levels of state assignment—the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as mortality and infection dynamics, and the pathogen detection processes. We provide code to simulate and analyse datasets with various underlying ecological processes and fit our model to a mark–recapture dataset of Mixophyes fleayi (Fleay's barred frog) infected with the amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd). In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting Bd, pathogen detection was highly dependent on infection intensity and false positives were non‐negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4–5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model. Our results highlight that incorporating state assignment errors improves inference on the ecological process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modelling state uncertainty in related models. For disease‐structured multistate models, we recommend conducting robust design surveys and collecting samples during each capture event to facilitate incorporating pathogen detection errors.

Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence – Insights from long-term surveillance

Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3–4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.

The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices

BackgroundThe COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths.MethodsWe fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020–30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.ResultsA model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000–420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%.ConclusionsLimitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

Burden of trichomoniasis among older adults in the United States: a systematic review.

Despite being one of the most common sexually transmitted infections (STIs) in the United States, the epidemiology of trichomoniasis remains understudied. One population that has been historically overlooked regarding STIs is that of older adults, despite many individuals remaining sexually active well into their older years. We investigated the reported prevalence and incidence of trichomoniasis in adults aged ≥45years in the United States using a systematic literature review. Twelve articles were included in the review, all assessing prevalence of trichomoniasis in this age group. Notably, no included articles assessed trichomoniasis incidence. Data collected encompassed several decades, from 1993 to 2016. Estimates of infection prevalence varied widely and ranged from 0.2% to 21.4% in included populations, with the highest prevalence typically seen among individuals seeking diagnostic testing for STIs. Several studies found increased risk for trichomoniasis in older patients compared to younger age groups. This is the first review to examine the risk of trichomoniasis in older adults, and the surprisingly high prevalence suggests that older adults may merit increased screening for trichomoniasis and sexual health education.

Impact of Annual versus Semiannual Mass Drug Administration with Ivermectin and Albendazole on Helminth Infections in Southeastern Liberia.

ABSTRACT.We compared the impact of three rounds of annual and five rounds of semiannual mass drug administration (MDA) with albendazole plus ivermectin on helminthic infections in Liberia. Repeated annual cross-sectional community surveys were conducted between 2013 and 2019 in individuals of 5 years and older. Primary outcome was the change of infection prevalence estimates from baseline to month 36 (12 months after the last treatment). After three rounds of annual MDA, Wuchereria bancrofti circulating filarial antigen (CFA) and microfilaria (Mf) prevalence estimates decreased from 19.7% to 4.3% and from 8.6% to 0%, respectively; after semiannual MDA, CFA and Mf prevalences decreased from 37.8% to 16.8% and 17.9% to 1%, respectively. Mixed effects logistic regression models indicated that the odds of having Mf decreased by 97% (P < 0.001) at month 36 (similar odds for annual and semiannual MDA zones). A parallel analysis showed that the odds of CFA were reduced by 83% and 69% at 36 months in the annual and semiannual treatment zones, respectively (P < 0.001). Onchocerca volvulus Mf prevalence decreased slightly after multiple MDA rounds in both treatment zones. Reductions in hookworm and Trichuris trichiura prevalences and intensities were slightly greater in the annual treatment zone. Ascaris lumbricoides prevalence rates were relatively unchanged, although infection intensities decreased sharply throughout. Results show that annual and semiannual MDA were equally effective for reducing LF and soil-transmitted helminth infection parameters over a 3-year period, and reductions recorded at month 36 were sustained by routine annual MDA through month 72.

Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States.

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.

A Bayesian analysis of a Test and Vaccinate or Remove study to control bovine tuberculosis in badgers (Meles meles).

A novel five year Test and Vaccinate or Remove (TVR) wildlife research intervention project in badgers (Meles meles) commenced in 2014 in a 100km2 area of Northern Ireland. It aimed to increase the evidence base around badgers and bovine TB and help create well-informed and evidence-based strategies to address the issue of cattle-to-cattle spread and spread between cattle and badgers. It involved real-time trap-side testing of captured badgers and vaccinating those that tested negative for bTB (BadgerBCG–BCG Danish 1331) and removal of those that tested bTB positive using the Dual-Path Platform VetTB test (DPP) for cervids (Chembio Diagnostic Systems, Medford, NY USA). Four diagnostic tests were utilised within the study interferon gamma release assay (IGRA), culture (clinical samples and post mortem), DPP using both whole blood and DPP using serum. BCG Sofia (SL222) was used in the final two years because of supply issues with BadgerBCG. Objectives for this study were to evaluate the performance of the DPP in field conditions and whether any trend was apparent in infection prevalence over the study period. A Bayesian latent class model of diagnostic test evaluation in the absence of a gold standard was applied to the data. Temporal variation in the sensitivity of DPP and interferon gamma release assay (IGRA) due to the impact of control measures was investigated using logistic regression and individual variability was assessed. Bayesian latent class analysis estimated DPP with serum to have a sensitivity of 0.58 (95% CrI: 0.40–0.76) and specificity of 0.97 (95% CrI: 0.95–0.98). The DPP with whole blood showed a higher sensitivity (0.69 (95% CrI: 0.48–0.88)) but similar specificity (0.98 (95% Crl: 0.96–0.99)). The change from BCG Danish to BCG Sofia significantly impacted on DPP serum test characteristics. In addition, there was weak evidence of increasing sensitivity of IGRA over time and differences in DPP test sensitivity between adults and cubs. An exponential decline model was an appropriate representation of the infection prevalence over the 5 years, with a starting prevalence of 14% (95% CrI: 0.10–0.20), and an annual reduction of 39.1% (95% CrI: 26.5–50.9). The resulting estimate of infection prevalence in year 5 of the study was 1.9% (95% CrI: 0.8–3.8). These results provide field evidence of a statistically significant reduction in badger TB prevalence supporting a TVR approach to badger intervention. They give confidence in the reliability and reproducibility in the DPP Whole Blood as a real time trap-side diagnostic test for badgers, and describe the effect of vaccination and reduced infection prevalence on test characteristics.

Detection and genetic characterization of community-based SARS-CoV-2 infections – New York City, March 2020

To limit SARS-CoV-2 introduction, the United States restricted travel from China on February 2 and from Europe on March 13, 2020 By March 15, community transmission was widespread in New York City (NYC) The NYC Department of Health and Mental Hygiene conducted sentinel surveillance of influenza-like symptoms (ILS) and genetic sequencing to characterize community transmission and determine the geographic origin of SARS-CoV-2 infections Among 544 specimens tested from persons with ILS and negative influenza test results, 36 (6 6%) were positive Genetically sequenced positive specimens most closely resembled sequences circulating in Europe Partnering with health care facilities and establishing systems for sentinel surveillance with capacity for genetic sequencing before an outbreak can inform timely public health response strategies

SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China.

We estimated SARS-CoV-2 infection prevalence in cohorts of repatriated citizens from Wuhan to be 0.44% (95% CI: 0.19%-1.03%). Although not representative of the wider population we believe these estimates are helpful in providing a conservative estimate of infection prevalence in Wuhan City, China, in the absence of large-scale population testing early in the epidemic.

Prevalence and Coinfection of Three Tick-Borne Pathogens in Questing Adult Blacklegged Ticks Ixodes scapularis (Vilas County, Wisconsin).

Introduction: In North America, the blacklegged tick (Ixodes scapularis) is a vector of several human pathogens, and tick-borne disease incidence is increasing. Objectives: We estimated the prevalence of questing blacklegged ticks vectoring three zoonotic pathogens in Vilas County, Wisconsin. Materials and Methods: We collected 461 adult blacklegged ticks and used PCR to screen for the presence of pathogens that cause Lyme disease (Borrelia burgdorferi), human granulocytic anaplasmosis (HGA, Anaplasma phagocytophilum), and babesiosis (Babesia microti). Results: We found that 52.5% of ticks carried at least one pathogen. The estimated infection prevalence in the tick population was 17.4% (Lyme disease), 14.3% (HGA), and 6.5% (babesiosis). Multiple pathogens were present in 14.3% of ticks surveyed. Conclusion: About half of questing ticks tested in this study carried at least one zoonotic pathogen. Coinfection was common in our study area and, if not properly recognized, leads to greater risk of underdiagnosis.

Antibody response to Schistosoma haematobium and other helminth species in malaria-exposed populations from Burkina Faso

Infection with helminths in sub-Saharan Africa could modulate the immune response towards Plasmodium falciparum as well as susceptibility to malaria infection and disease. The aim of this study is to assess the antibody responses to helminths species in malaria-exposed populations from Burkina Faso.Plasma samples were collected in rural villages inhabited by Fulani, Mossi and Rimaibe communities, and IgG against parasitic helminths were measured by ELISA. The prevalence of IgG against antigens of Strongyloides stercoralis, Wuchereria bancrofti and Schistosoma haematobium (Soluble Egg Antigen, SEA) was 5%, 16% and 63% respectively, in line with estimates of infection prevalence in the region for the three parasites. Anti-SEA IgG prevalence was highest at 10–20 years of age, higher in males than females, and did not show differences between ethnic groups. However, the Fulani showed lower levels of anti-SEA IgG suggesting that lighter S. haematobium infections may occur in the ethnic group known for a marked lower susceptibility to P. falciparum.The present data support the use of serological methods for integrated surveillance of neglected tropical diseases such as soil-transmitted helminths, lymphatic filariasis and bilharzia. Furthermore, as helminth infections might promote downregulation of immune responses against intracellular pathogens, the observation of lower anti-SEA IgG levels in the malaria resistant Fulani population warrants further investigation into the immunological cross-talk between S. haematobium and P. falciparum in this geographical region.

COVID-19 in Italy: impact of containment measures and prevalence estimates of infection in the general population.

Since the beginning of the COVID-19 epidemic in Italy, the Italian Government implemented several restrictive measures to contain the spread of the infection. Data shows that, among these measures, the lockdown implemented as of 9 March had a positive impact, in particular the central and southern regions of Italy, while other actions appeared to be less effective. When the true prevalence of a disease is unknown, it is possible estimate it, based on mortality data and the assumptive case-fatality rate of the disease. Given these assumptions, the estimated period-prevalence of COVID-19 in Italy varies from 0.35% in Sicily to 13.3% in Lombardy.