Data Of Reported Cases Research Articles

Respiratory syncytial virus infection notification trends and interpretation of the reported case data, 2018-2021, Japan.

In Japan, as elsewhere, the COVID-19 pandemic affected the notification trends of respiratory syncytial virus (RSV) infection. Here, we describe the epidemiological trends of RSV cases among children reported during 2018-2021 in Japan, based on the national surveillance system. Compared to 2018 and 2019, 2020 saw an unprecedented decrease in RSV notifications per sentinel site. However, 2021 experienced an unseasonably early and high peak in week 28 (peak week in 2018 and 2019: week 37) with a large resurgence in notifications, nationwide and across regions. Regarding age, compared to 2018 and 2019, the number and proportion of cases aged 2, 3, and ≥4-years increased substantially in 2021 but the number of cases aged <1 year decreased slightly. Furthermore, in 2021, the ratio of notifications per site from outpatient clinics to hospitals increased, suggesting a proportionate increase in clinically milder case diagnoses. Notably, RSV-attributed deaths from vital statistics also dropped substantially in 2020 and rebounded in 2021, but were fewer than in 2018 or 2019. While RSV incidence likely declined in 2020 (possibly from COVID-19 countermeasures) and increased in 2021, notifications in 2021 appeared to be associated with milder presentations. Given unpredictable RSV epidemiology, continuous monitoring and pluralistic assessments are imperative.

Estimating the Burden and Distribution of Post-COVID-19 Condition in Washington State, March 2020-October 2023.

After SARS-CoV-2 infection, some people will experience long-term sequelae known as post-COVID-19 condition (PCC). Although PCC is recognized as a public health problem, estimates of the prevalence of PCC are sparse. We described a framework for estimating the incidence and prevalence of PCC by population subgroups and geography over time in Washington State. We collected data on reported COVID-19 cases and hospitalizations and estimated SARS-CoV-2 infections in Washington State from March 2020 through October 2023. The reported case data were incorporated with parameter estimates from published articles and prevalence estimates from the Household Pulse Survey into a mathematical compartmental model of PCC progression. The model used differential equations to describe how the population of people with PCC moved through the model's various stages. This framework allowed us to integrate data on age group, sex, race and ethnicity, vaccination status, and county to estimate incidence and prevalence of PCC for each subgroup. Our model indicated that 6.4% (95% CI, 5.9%-6.8%) of all adults in Washington State were experiencing PCC as of October 2023. In addition to temporal differences in PCC prevalence and incidence, we found substantial differences across age groups, race and ethnicity, and sex. Geographic heterogeneity was pronounced, with the highest rates of PCC in central and eastern Washington. Estimation of PCC prevalence is essential for addressing PCC as a public health problem. Responding to PCC will require continued surveillance, research, and dedicated financial and public health action. This analysis, accounting for heterogeneities, highlights disparities in the prevalence, incidence, and distribution of PCC in Washington State and can better guide awareness and response efforts.

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

A tale of two countries: Optimal control and cost-effectiveness analysis of monkeypox disease in Germany and Nigeria

The Monkeypox (Mpox) virus started developing as potentially fatal in Spring 2022. Mpox is a zoonotic disease that can spread between animals and people. While the cases in Nigeria are on the rise, Germany has become one of Europe’s most impacted nations. Mathematical modeling is useful for monitoring, managing, and predicting infectious disease epidemics. This study incorporates the transmission rate of human-to-animal and animal-to-animal in a compartmental mathematical model of Mpox to capture the current trend in transmission for wildlife and human preservation. Parameters were estimated while the model was fitted to reported case data of Mpox from Nigeria and Germany to capture the trend in Europe and West Africa. Analyses such as optimal control, sensitivity, and cost-effectiveness were carried out. The results show that the best strategy to combat the spread of the virus is through changing behavior and personal hygiene among humans.

Epidemiological research on rare diseases using large-scale online search queries and reported case data

BackgroundRare diseases have become a major public health concern worldwide. However, detailed epidemiological data are lacking. With the development of the Internet, search queries have played an important role in disease surveillance. In this study, we explored a new method for the epidemiological research on rare diseases, using large-scale online search queries and reported case data. We distilled search logs related to rare diseases nationwide from 2016 to 2019. The case data were obtained from China’s national database of rare diseases during the same period.ResultsA total of 120 rare diseases were included in this study. From 2016 to 2019, the number of patients with rare diseases estimated using search data and those obtained from the case database showed an increasing trend. Rare diseases can be ranked by the number of search estimated patients and reported patients, and the rankings of each disease in both search and reported case data were generally stable. Furthermore, the disease rankings in the search data were relatively consistent with the reported case data in each year, with more than 50% of rare diseases having a ranking difference of -20 to 20 between the two systems. In addition, the relationship between the disease rankings in the two systems was generally stable over time. Based on the relationship between the disease rankings in the search and reported case data, rare diseases can be classified into two categories.ConclusionOnline search queries may provide an important new resource for detecting rare diseases. Rare diseases can be classified into two categories to guide different epidemiological research strategies.

Dynamic modeling and data fitting of climatic and environmental factors and people's behavior factors on hand, foot, and mouth disease (HFMD) in Shanghai, China

BackgroundHand, foot, and mouth disease (HFMD) appear to be a multi-wave outbreak with unknown mechanisms. We investigate the effects of climatic and environmental factors and changes in people's behavior factors that may be caused by external factors: temperature, relative humidity, and school opening and closing. MethodsDistributed lag nonlinear model (DLNM) and dynamic model are used to research multi-wave outbreaks of HFMD. Climatic and environmental factors impact on transmission rate β(t) is modeled through DLNM and then substituted into this relationship to establish the dynamic model with reported case data to test for validity. ResultsRelative risk (RR) of HFMD infection increases with increasing temperature. The RR of infection first increases and then decreases with the increase of relative humidity. For the model fitting HFMD dynamic, time average basic reproduction number [R0] of Stage I (without vaccine) and Stage II (with EV71 vaccine) are 1.9362 and 1.5478, respectively. Temperature has the highest explanatory power, followed by school opening and closing, and relative humidity. ConclusionWe obtain three conclusions about the prevention and control of HFMD. 1) According to the temperature, relative humidity and school start time, the outbreak peak of HFMD should be warned and targeted prevention and control measures should be taken. 2) Reduce high indoor temperature when more than 31.5 oC, and increase low relative humidity when less than 77.5% by opening the window for ventilation, adding houseplants, using air conditioners and humidifiers, reducing the incidence of HFMD and the number of infections. 3) The risk of HFMD transmission during winter vacations is higher than during summer vacations. It is necessary to strengthen the publicity of HFMD prevention knowledge before winter vacations and strengthen the disinfection control measures during winter vacations in children's hospitals, school classrooms, and other places where children gather to reduce the frequency of staff turnover during winter vacations.

Divergence of wastewater SARS-CoV-2 and reported laboratory-confirmed COVID-19 incident case data coincident with wide-spread availability of at-home COVID-19 antigen tests.

Concentrations of SARS-CoV-2 RNA in wastewater settled solids from publicly owned treatment works (POTWs) historically correlated strongly with laboratory confirmed incident COVID-19 case data. With the increased availability of at-home antigen tests since late 2021 and early 2022, laboratory test availability and test seeking behavior has decreased. In the United States, the results from at-home antigen tests are not typically reportable to public health agencies and thus are not counted in case reports. As a result, the number of reported laboratory-confirmed incident COVID-19 cases has decreased dramatically, even during times of increased test positivity rates and wastewater concentrations of SARS-CoV-2 RNA. Herein, we tested whether the correlative relationship between wastewater concentrations of SARS-CoV-2 RNA and reported laboratory-confirmed COVID-19 incidence rate has changed since 1 May 2022, a point in time immediately before the onset of the BA.2/BA.5 surge, the first surge to begin after at-home antigen test availability was high in the region. We used daily data from three POTWs in the Greater San Francisco Bay Area of California, USA for the analysis. We found that although there is a significant positive association between wastewater measurements and incident rate data collected after 1 May 2022, the parameters describing the relationship are different than those describing the relationship between the data collected prior to 1 May 2022. If laboratory test seeking or availability continues to change, the relationship between wastewater and reported case data will continue to change. Our results suggest, assuming SARS-CoV-2 RNA shedding remains relatively stable among those infected with the virus as different variants emerge, that wastewater concentrations of SARS-CoV-2 RNA can be used to estimate COVID-19 cases as they would have been during the time when laboratory testing availability and test seeking behavior were at a high (here, before 1 May 2022) using the historical relationship between SARS-CoV-2 RNA and COVID-19 case data.

Cerebral venous thrombosis (CVT) complicating tubercular meningitis

BackgroundTuberculous meningitis (TBM), complicated with cerebral venous thrombosis (CVT), has been sparsely reported and needs to be investigated further.MethodsAmong those with tuberculous meningitis in Haihe Hospital, Tianjin University, 3 patients with venous sinus thrombosis were identified retrospectively. “Tuberculous meningitis” and “cerebral venous thrombosis” were used as keywords, and the retrieved literature was summarized and analyzed. Our data were combined with previously reported case data to describe this new condition.ResultsAmong 28 patients with a median onset age of 31 years for TBM, 17 were females. The manifestations were fever, headache, and seizure. Magnetic resonance imaging (MRI) venography showed that the most common site of venous sinus thrombosis involved superior sagittal sinus, left transverse sinus, left sigmoid sinus, cavernous sinus, and straight sinus. The abnormalities found on MRI include hydrocephalus, exudates, hemorrhage, meningeal enhancement, infarction, and tuberculoma. In the acute phase, all patients received standard anti-TB treatment, and 14/28 patients received anticoagulant treatment. The mortality rate of these patients was 17.9%, and 21/28 (75%) became functionally independent.ConclusionsCVT is one of the rare complications of TMB and must be considered a differential diagnosis in patients with TBM who show poor clinical features and/or develop new neurological signs.

Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study

BackgroundThe continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves.MethodsWe initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants.ResultsThe necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged.ConclusionsShanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as Then, the developed model is used to create a spatiotemporal data set of values at the county level in the United States. Second, it investigates relationships between and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios.

Community structured model for vaccine strategies to control COVID19 spread: A mathematical study.

Initial efforts to mitigate the COVID-19 pandemic have relied heavily on non-pharmaceutical interventions (NPIs), including physical distancing, hand hygiene, and mask-wearing. However, an effective vaccine is essential to containing the spread of the virus. We developed a compartmental model to examine different vaccine strategies for controlling the spread of COVID-19. Our framework accounts for testing rates, test-turnaround times, and vaccination waning immunity. Using reported case data from the city of Toronto, Canada between Mar-Dec, 2020 we defined epidemic phases of infection using contact rates as well as the probability of transmission upon contact. We investigated the impact of vaccine distribution by comparing different permutations of waning immunity, vaccine coverage and efficacy throughout various stages of NPI’s relaxation in terms of cases and deaths. The basic reproduction number is also studied. We observed that widespread vaccine coverage substantially reduced the number of cases and deaths. Under phases with high transmission, an early or late reopening will result in new resurgence of the infection, even with the highest coverage. On the other hand, under phases with lower transmission, 60% of coverage is enough to prevent new infections. Our analysis of R0 showed that the basic reproduction number is reduced by decreasing the tests turnaround time and transmission in the household. While we found that household transmission can decrease following the introduction of a vaccine, public health efforts to reduce test turnaround times remain important for virus containment.

Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study.

BackgroundThe COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic in the long term, it is pivotal to understand what factors drive prevalence rates and to predict the future trajectory of the virus.ObjectiveThis study had 2 objectives. First, it tested the statistical relationship between socioeconomic status and COVID-19 prevalence. Second, it used machine learning techniques to predict cumulative COVID-19 cases in a multicountry sample of 182 countries. Taken together, these objectives will shed light on socioeconomic status as a global risk factor of the COVID-19 pandemic.MethodsThis research used exploratory data analysis and supervised machine learning methods. Exploratory analysis included variable distribution, variable correlations, and outlier detection. Following this, the following 3 supervised regression techniques were applied: linear regression, random forest, and adaptive boosting (AdaBoost). Results were evaluated using k-fold cross-validation and subsequently compared to analyze algorithmic suitability. The analysis involved 2 models. First, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 reported case data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index (HDI) metrics of life expectancy, mean years of schooling, expected years of schooling, and gross national income were used to approximate socioeconomic status.ResultsAll variables correlated positively with the 2021 COVID-19 prevalence, with R2 values ranging from 0.55 to 0.85. Using socioeconomic indicators, COVID-19 prevalence was predicted with a reasonable degree of accuracy. Using 2020 reported case rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R2=0.543). When socioeconomic indicators were added alongside 2020 prevalence rates as features, the average predictive performance improved considerably (R2=0.721) and all error statistics decreased. Thus, adding socioeconomic indicators alongside 2020 reported case data optimized the prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R2=0.693 on the first model and R2=0.763 on the second model, followed by random forest (0.481 and 0.722) and AdaBoost (0.454 and 0.679). Following this, the second model was retrained using a selection of additional COVID-19 risk factors (population density, median age, and vaccination uptake) instead of the HDI metrics. However, average accuracy dropped to 0.649, which highlights the value of socioeconomic status as a predictor of COVID-19 cases in the chosen sample.ConclusionsThe results show that socioeconomic status is an important variable to consider in future epidemiological modeling, and highlights the reality of the COVID-19 pandemic as a social phenomenon and a health care phenomenon. This paper also puts forward new considerations about the application of statistical and machine learning techniques to understand and combat the COVID-19 pandemic.

How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey

A serological survey from CDC revealed more than 10% of individuals in America probably resolving or past infection with SARS-CoV-2 at the end of 2020, which illustrated there were massive unconfirmed asymptomatic infected people by contrast with the reported cases numbers. Asymptomatic patients as one of the crucial reasons for the COVID-19 pandemic being tough to contain, estimating the number of unconfirmed ones including the active infected and having cured in this population, is of great guiding significance for formulating epidemic prevention and control policies. This paper proposes a varying coefficient Susceptible–Infected–Removed–Susceptible (vSIRS) model to obtain the time series data of the unconfirmed asymptomatic infected numbers. Moreover, due to the time-varying coefficients, we can effectively track the situation changes of the COVID-19 intervened by related policy support and medical care level through this epidemiological model. A novel two-stage approach with a programming problem is correspondingly developed to accomplish the estimation of the unknown parameters in the vSIRS model. Subsequently, by leveraging seroprevalence data, daily reported cases data, and other clinical information, we apply the vSIRS model to analyze the evolution of COVID-19 in America. The modeling results show millions of active asymptomatic infected individuals were unconfirmed during the autumn and winter of 2020, which was a momentous factor for driving American COVID-19 pandemic.

Estimation of local transmissibility in the early phase of monkeypox epidemic in 2022

ObjectivesTo estimate the basic reproductive number (Ro) to help us understand and control the spread of monkeypox in immunologically naive populations. MethodsUsing three highest incidence populations including England, Portugal, and Spain as examples as of 18 June 2022, we employed the branching process with a Poisson likelihood and gamma-distributed serial interval to fit daily reported case data of monkeypox to estimate Ro. Sensitivity analyses were performed by varying mean serial interval from 6.8 to 12.8 days. ResultsThe median posterior estimates of Ro for monkeypox in the three study populations were statistically >1 (England: Ro = 1.60 [95% (credible interval) CrI, 1.50–1.70]; Portugal: Ro = 1.40 [95% CrI, 1.20–1.60]; Spain: Ro = 1.80 [95% CrI, 1.70–2.00]). Ro estimates varied over 1.30 to 2.10, depending on the serial interval. DiscussionThe updated Ro estimates across different populations will inform policy makers' plans for public health control measures. Currently, monkeypox has a sustainable outbreak potential and may challenge healthcare systems, mainly due to declines in the population level immunity to Orthopoxviruses since the cessation of routine smallpox vaccination. Smallpox vaccination has been shown to be effective in protecting (≤85% effectiveness) against monkeypox infection in earlier times. So early postexposure vaccination is currently being offered in an attempt to control its spread.

On the use of the reproduction number for SARS-CoV-2: Estimation, misinterpretations and relationships with other ecological measures.

The basic reproduction number, R 0, and its real‐time analogue, Rt , are summary measures that reflect the ability of an infectious disease to spread through a population. Estimation methods for Rt have a long history, have been widely developed and are now enhanced by application to the COVID‐19 pandemic. While retrospective analyses of Rt have provided insight into epidemic dynamics and the effects of control strategies in prior outbreaks, misconceptions around the interpretation of Rt have arisen with broader recognition and near real‐time monitoring of this parameter alongside reported case data during the COVID‐19 pandemic. Here, we discuss some widespread misunderstandings regarding the use of Rt as a barometer for population risk and its related use as an ‘on/off’ switch for policy decisions regarding relaxation of non‐pharmaceutical interventions. Computation of Rt from downstream data (e.g. hospitalizations) when infection counts are unreliable exacerbates lags between when transmission happens and when events are recorded. We also discuss analyses that have shown various relationships between Rt and measures of mobility, vaccination coverage and a test–trace–isolation intervention in different settings.

The impact of three progressively introduced interventions on second wave daily COVID-19 case numbers in Melbourne, Australia

BackgroundThe city of Melbourne, Australia experienced two waves of the COVID-19 epidemic peaking, the first in March and a more substantial wave in July 2020. During the second wave, a series of control measure were progressively introduced that initially slowed the growth of the epidemic then resulted in decreasing cases until there was no detectable local transmission.MethodsTo determine the relative efficacy of the progressively introduced intervention measures, we modelled the second wave as a series of exponential growth and decay curves. We used a linear regression of the log of daily cases vs time, using a four-segment linear spline model corresponding to implementation of the three successive major public health measures. The primary model used all reported cases between 14 June and 15 September 2020 then compared the projection of the model with observed cases predicting future case trajectory up until the 31 October 2020 to assess the use of exponential models in projecting the future course and planning future interventions. The main outcome measures were the exponential daily growth constants, analysis of residuals and estimates of the 95% confidence intervals for the expected case distributions, comparison of predicted daily cases.ResultsThe exponential growth/decay constants in the primary analysis were: 0.122 (s.e. 0.004), 0.035 (s.e. 0.005), − 0.037 (s.e. 0.011), and − 0.069 (s.e. 0.003) for the initial growth rate, Stage 3, Stage 3 + compulsory masks and Stage 4, respectively. Extrapolation of the regression model from the 14 September to the 31 October matched the decline in observed cases over this period.ConclusionsThe four-segment exponential model provided an excellent fit of the observed reported case data and predicted the day-to-day range of expected cases. The extrapolated regression accurately predicted the decline leading to epidemic control in Melbourne.

Integrating Public Health Surveillance and Environmental Data to Model Presence of Histoplasma in the United States.

Background:In the United States, the true geographic distribution of the environmental fungus Histoplasma capsulatum remains poorly understood but appears to have changed since it was first characterized. Histoplasmosis is caused by inhalation of the fungus and can range in severity from asymptomatic to life threatening. Due to limited public health surveillance and under detection of infections, it is challenging to directly use reported case data to characterize spatial risk.Methods:Using monthly and yearly county-level public health surveillance data and various environmental and socioeconomic characteristics, we use a spatio-temporal occupancy model to estimate latent, or unobserved, presence of H. capsulatum, accounting for imperfect detection of histoplasmosis cases.Results:We estimate areas with higher probabilities of the presence of H. capsulatum in the East North Central states around the Great Lakes, reflecting a shift of the endemic region to the north from previous estimates. The presence of H. capsulatum was strongly associated with higher soil nitrogen levels.Conclusions:In this investigation, we were able to mitigate challenges related to reporting and illustrate a shift in the endemic region from historical estimates. This work aims to help inform future surveillance needs, clinical awareness, and testing decisions for histoplasmosis.

Modeling Vaccine Efficacy for COVID-19 Outbreak in New York City

Simple SummaryThis article aims to study the COVID-19 data for New York City. We use both the daily number of second dose vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.In this article we study the efficacy of vaccination in epidemiological reconstructions of COVID-19 epidemics from reported cases data. Given an epidemiological model, we developed in previous studies a method that allowed the computation of an instantaneous transmission rate that produced an exact fit of reported cases data of the COVID-19 outbreak. In this article, we improve the method by incorporating vaccination data. More precisely, we develop a model in which vaccination is variable in its effectiveness. We develop a new technique to compute the transmission rate in this model, which produces an exact fit to reported cases data, while quantifying the efficacy of the vaccine and the daily number of vaccinated. We apply our method to the reported cases data and vaccination data of New York City.

Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 During the First Wave in the United States.

BackgroundIt remains unclear how changes in human mobility shaped the transmission dynamic of coronavirus disease 2019 (COVID-19) during its first wave in the United States. MethodsBy coupling a Bayesian hierarchical spatiotemporal model with reported case data and Google mobility data at the county level, we found that changes in movement were associated with notable changes in reported COVID-19 incidence rates about 5 to 7 weeks later. ResultsAmong all movement types, residential stay was the most influential driver of COVID-19 incidence rate, with a 10% increase 7 weeks ago reducing the disease incidence rate by 13% (95% credible interval, 6%–20%). A 10% increase in movement from home to workplaces, retail and recreation stores, public transit, grocery stores, and pharmacies 7 weeks ago was associated with an increase of 5%–8% in the COVID-10 incidence rate. In contrast, parks-related movement showed minimal impact. ConclusionsPolicy-makers should anticipate such a delay when planning intervention strategies restricting human movement.