Case Counts Research Articles

Estimation of the Time-Varying Effective Reproductive Number of COVID-19 Based on Multivariate Time Series of Severe Health Outcomes.

The time-varying effective reproduction number (Rt at time t) measures the transmissibility of SARS-CoV-2 and is conventionally based on daily case counts, which may suffer from time-varying ascertainment. We analyzed Rt estimates from case counts and severe COVID-19 (intensive care unit admissions, severe or critical cases, and mortality) across 2022 in Hong Kong's fifth and sixth waves of infection. Within the fifth wave, the severe disease-based Rt (3.5) was significantly higher than the case-based Rt (2.4) but not in the sixth wave. During periods with fluctuating underreporting, data based on severe diseases may provide more reliable Rt estimates.

Comparison of surveillance systems for monitoring COVID-19 in England: a retrospective observational study

During the COVID-19 pandemic, cases were tracked using multiple surveillance systems. Some systems were completely novel, and others incorporated multiple data streams to estimate case incidence and prevalence. How well these different surveillance systems worked as epidemic indicators is unclear, which has implications for future disease surveillance and outbreak management. The aim of this study was to compare case counts, prevalence and incidence, timeliness, and comprehensiveness of different COVID-19 surveillance systems in England. For this retrospective observational study of COVID-19 surveillance systems in England, data from 12 surveillance systems were extracted from publicly available sources (Jan 1, 2020-Nov 30, 2021). The main outcomes were correlations between different indicators of COVID-19 incidence or prevalence. These data were integrated as daily time-series and comparisons undertaken using Spearman correlation between candidate alternatives and the most timely (updated daily, clinical case register) and the least biased (from comprehensive household sampling) COVID-19 epidemic indicators, with comparisons focused on the period of Sept 1, 2020-Nov 30, 2021. Spearman statistic correlations during the full focus period between the least biased indicator (from household surveys) and other epidemic indicator time-series were 0·94 (95% CI 0·92 to 0·95; clinical cases, the most timely indicator), 0·92 (0·90 to 0·94; estimates of incidence generated after incorporating information about self-reported case status on the ZoeApp, which is a digital app), 0·67 (95% CI 0·60 to 0·73, emergency department attendances), 0·64 (95% CI 0·60 to 0·68, NHS 111 website visits), 0·63 (95% CI 0·56 to 0·69, wastewater viral genome concentrations), 0·60 (95% CI 0·52 to 0·66, admissions to hospital with positive COVID-19 status), 0·45 (95% CI 0·36 to 0·52, NHS 111 calls), 0·08 (95% CI -0·03 to 0·18, Google search rank for "covid"), -0·04 (95% CI -0·12 to 0·05, in-hours consultations with general practitioners), and -0·37 (95% CI -0·46 to -0·28, Google search rank for "coronavirus"). Time lags (-14 to +14 days) did not markedly improve these rho statistics. Clinical cases (the most timely indicator) captured a more consistent proportion of cases than the self-report digital app did. A suite of monitoring systems is useful. The household survey system was the most comprehensive and least biased epidemic monitor, but not very timely. Data from laboratory testing, the self-reporting digital app, and attendances to emergency departments were comparatively useful, fairly accurate, and timely epidemic trackers. National Institute for Health and Care Research Health Protection Research Unit in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King's College London, and the University of East Anglia.

Vaccine Mandates and Influenza Vaccination During the Pandemic.

To determine whether a state influenza vaccine mandate and elevated community coronavirus disease 2019 (COVID-19) severity affected a child's probability of receiving an influenza vaccine during the 2020-2021 influenza season, given the child's previous vaccination history. Longitudinal cohort study using enrollment and claims data of 71 333 children aged 6 months to 18 years living in Massachusetts, New Hampshire, and Maine, from a regional insurer. Schoolchildren in Massachusetts were exposed to a new influenza vaccine mandate in the 2020-2021 season. Community COVID-19 severity was measured using county-level total cumulative confirmed case counts between March 2020 and August 2020 and linked by zip codes. The primary outcome of interest was a claim for any influenza vaccine in the 2020-2021 season. Children living in a state with a vaccine mandate during the 2020-2021 influenza season had a higher predicted probability of receiving an influenza vaccine than those living in states without a mandate (47.7%, confidence interval 46.4%-49.0%, vs 21.2%, confidence interval 18.8%-23.6%, respectively, for previous nonvaccinators, and 78.2%, confidence interval 77.4%-79.0%, vs 58.2%, confidence interval 54.7%-61.7%, for previous vaccinators); the difference was 6.5 percentage points greater among previous nonvaccinators (confidence interval 1.3%-11.7%). Previously vaccinated children had a lower predicted probability of receiving an influenza vaccine if they lived in a county with the highest COVID-19 severity compared with a county with low COVID-19 severity (72.1%, confidence interval 70.5%-73.7%, vs 77.3%, confidence interval 74.7%-79.9%). Strategies to improve uptake of influenza vaccination may have differential impact based on previous vaccination status and should account for community factors.

Understanding the importance of spatial correlation in identifying spatio-temporal variation of disease risk, in the case of malaria risk mapping in southern Ethiopia

Malaria remains a major health problem in developing countries despite a significant reduction in incidence in the last few years. Disease mapping thus helps to understand the spatial pattern and identify areas characterized by unusual risks. Several spatial models have been used to analyze the incidence of malaria. We aim to compare the predictive performance of these models and investigate the effect of ignoring spatial correlation. The reported malaria case counts of genus P.falciparum in 149 districts of southern Ethiopia from January 2016 to May 2019 were analyzed using the spatial time series model (STS) that ignores spatial correlation, Spatio-temporal conditional autoregressive model (STCAR), Spatio-temporal geostatistical model (STG) and Spatio-temporal spatial discrete approximation to log Gaussian cox process (STSDALGCP). We assess the predictive performance of the models using root mean square error, mean absolute error, and coverage probability. We found that monthly average rainfall, temperature, humidity, and EVI are significantly associated with malaria risk. The spatial variation of malaria incidence changes with time, in particular, the high incidence was observed from November to December, months after heavy rainfall, and more pronounced in the southwest of the country. STSDALGCP gives a small prediction error in test set and captures the uncertainties better than other models, while the STS model gives a high prediction error. Accounting for spatial correlation is crucial for disease risk mapping and leads to better prediction of disease risk. Since malaria transmission operates in a spatially continuous manner, a spatially continuous model should be considered when it is computationally feasible.

PanDa Game: Optimized Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model.

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data are typically de-identified before publication to protect the privacy of the corresponding individuals. However, current data publishing approaches for this type of data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies have the potential to both raise privacy risks or overprotect the data and impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies for the publication of individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) average performance of predicting future case counts; and 2) mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate the effectiveness of the new model. The results indicate that the game theoretic model outperforms all state-of-the-art baseline approaches, including those adopted by CDC, while maintaining low privacy risk. We further perform an extensive sensitivity analyses to show that our findings are robust to order-of-magnitude parameter fluctuations.

Recent Advances in the Prevention and Management of Monkeypox Viral Infection in Humans.

There have been several neglected infectious pathogens that have reemerged in the last few decades, including the monkeypox virus, a virus from the orthopoxviral genus that causes monkeypox and is transmitted between animals and humans. The human monkeypox outbreak has spread to several different countries. Because of the outbreak's unusually high case count and lack of connections to endemic nations, there are concerns that the monkeypox transmission pattern may have changed. The current study aimed to provide recent advancements in the prevention and management of the monkeypox virus in humans. We have highlighted recent advancements in the prevention and management of the monkeypox virus in humans in this work. For the treatment and prevention of monkeypox, new medications and vaccinations are being used, and more study is needed to understand the epidemiology, biology, and ecology of the virus in endemic regions and stop future global outbreaks. Vaccines available in the market for the treatment of viruses are JYNEOS and ACAM2000. Some of the antiviral drugs, such as tecovirimat, brincidofovir, cidofovir, trifluridine, and vaccinia immune globulin, are used for the treatment of the monkeypox virus. Some of the vaccines, such as NIOCH-14, Cidofovir, CMX-001, and ST-246, are currently in clinical trials. We have, herein, covered features of monkeypox viral biology that are important for risk assessment and getting ready for an outbreak of the monkeypox virus, with a focus on recent advances in knowledge of the virus's host range, evolutionary potential, and potential targets for neutralization.

Decreasing incidence of hepatocellular carcinoma among most racial groups: SEER-22, 2000-2019.

Hepatocellular carcinoma (HCC) incidence was rising in the United States. Previously, using data collected by the Surveillance, Epidemiology, and End Results (SEER) Program through 2017, we found that overall incidence had begun to decline, although not in Black and American Indian/Alaska Native (AI/AN) populations. Utilizing expanded SEER data encompassing ~50% of the population, we examined secular trends and demographic differences in HCC incidence through 2019. We included cases of HCC diagnosed in adults aged ≥20 years residing in SEER-22 registry areas. We examined case counts, incidence rates (per 100,000 person-years), annual percent changes (APCs), and calendar years when APCs changed significantly. HCC incidence increased from 5.56 in 2000 to 8.89 in 2009 (APC, 5.17%), then rose more slowly during 2009-2015 (APC, 2.28%). After peaking at 10.03 in 2015, incidence fell to 9.20 in 2019 (APC, -2.26%). In Asian/Pacific Islanders (A/PI), the decline began in 2007 and accelerated in 2015 (APCs: 2007-2015, -1.84%; 2015-2019, -5.80%). In 2014, incidence began to fall in the White (APC: 2014-2019, -1.11%) and Hispanic populations (APC: 2014-2019, -1.72%). In 2016, rates began to fall in Black individuals (APC: 2016-2019, -6.05%). In the AI/AN population, incidence was highest in 2017, although the subsequent decline was not statistically significant. In 2019, population-specific rates were: White, 6.94; Black, 10.74; A/PI, 12.11; AI/AN, 14.56; Hispanic, 15.48. HCC incidence is now decreasing in most US racial/ethnic populations, including among Black individuals. The onset of decline differed among racial/ethnic groups and wide disparities in HCC rates remain.

Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities.

SARS-CoV-2 PCR testing data has been widely used for COVID-19 surveillance. Existing COVID-19 forecasting models mainly rely on case counts obtained from qPCR results, even though the binary PCR results provide a limited picture of the pandemic trajectory. Most forecasting models have failed to accurately predict the COVID-19 waves before they occur. Recently a model utilizing cross-sectional population cycle threshold (Ct-the number of cycles required for the fluorescent signal to cross the background threshold) values obtained from PCR tests (Ct-based model) was developed to overcome the limitations of using only binary PCR results. In this study, we aimed to improve on COVID-19 forecasting models using features derived from the Ct-based model, to detect epidemic waves earlier than case-based trajectories. PCR data was collected weekly at Northeastern University (NU) between August 2020 and January 2022. Campus and county epidemic trajectories were generated from case counts. A novel forecasting approach was developed by enhancing a recent deep learning model with Ct-based features and applied in Suffolk County and NU campus. For this, cross-sectional Ct values from PCR data were used to generate Ct-based epidemic trajectories, including effective reproductive rate (Rt) and incidence. The improvement in forecasting performance was compared using absolute errors and residual squared errors with respect to actual observed cases at the 7-day and 14-day forecasting horizons. The model was also tested prospectively over the period January 2022 to April 2022. Rt curves estimated from the Ct-based model indicated epidemic waves 12 to 14 days earlier than Rt curves from NU campus and Suffolk County cases, with a correlation of 0.57. Enhancing the forecasting models with Ct-based information significantly decreased absolute error (decrease of 49.4 and 221.5 for the 7 and 14-day forecasting horizons) and residual squared error (40.6 and 217.1 for the 7 and 14-day forecasting horizons) compared to the original model without Ct features. Ct-based epidemic trajectories can herald an earlier signal for impending epidemic waves in the community and forecast transmission peaks. Moreover, COVID-19 forecasting models can be enhanced using these Ct features to improve their forecasting accuracy. In this study, we make the case that public health agencies should publish Ct values along with the binary positive/negative PCR results. Early and accurate forecasting of epidemic waves can inform public health policies and countermeasures which can mitigate spread.

Evaluation and Clinicopathological Correlation of ALDH1 in Colorectal Adenoma with Low-/High-Grade Dysplasia and Carcinoma.

Colorectal carcinoma (CRC) stands as one of the most prevalent malignant neoplasms, carrying significant morbidity and mortality implications. Within colorectal carcinogenesis, cancer stem cells are recognized as key contributors, infusing tumors with aggressive traits, including chemoresistance. A group of enzymes known as ALDH1 exhibits stem cell properties, potentially playing a role in colorectal neoplasms. This study aims to evaluate ALDH1 expression in colonic neoplasms and its correlation with clinicopathological parameters. The research encompasses 50 consecutive cases, involving CRC (30) and colorectal adenoma (20), gathered prospectively from September 2019 to August 2021, as well as archived cases from January 2018 to August 2019. Histological examination was conducted on CRC cases to assess tumor type, grade, lymphovascular invasion, perineural invasion, mitosis, and necrosis, while colorectal adenomas were subjected to histological grading. ALDH1 immunohistochemistry was performed on both CRC and adenoma specimens. Statistical analysis utilized SPSS 20 software, employing the chi-squared test and Fischer's exact test. A higher count of adenoma cases displayed positive staining (p = 0.0005) and greater expression (p = 0.036) in comparison to carcinoma cases. The other clinicopathological parameters didn't demonstrate notable associations. Adenomas with low-grade dysplasia exhibited a higher frequency of positive ALDH1 staining and expression than those with high-grade dysplasia. In malignant cases, a higher proportion of positive staining was observed in lower-stage disease compared to higher-stage disease. The heightened staining and expression outcomes of ALDH1 in adenomas versus carcinomas, as well as their presence in lower-stage carcinomas, suggest the potential acquisition of novel mutations and the proliferation of distinct clonal stem cell subsets during disease progression. The absence of ALDH1 in adenoma/carcinoma could indicate a poorer prognosis and an increased likelihood of disease progression to a higher stage. Comprehensive multi-institutional and validation studies are needed to enhance our understanding of ALDH1's role in colorectal oncogenesis, as well as its viability as a targeted or personalized therapy option.

Annual Report to the Nation on the Status of Cancer, part 2: Early assessment of the COVID-19 pandemic's impact on cancer diagnosis.

With access to cancer care services limited because of coronavirus disease 2019 control measures, cancer diagnosis and treatment have been delayed. The authors explored changes in the counts of US incident cases by cancer type, age, sex, race, and disease stage in 2020. Data were extracted from selected US population-based cancer registries for diagnosis years 2015-2020 using first-submission data from the North American Association of Central Cancer Registries. After a quality assessment, the monthly numbers of newly diagnosed cancer cases were extracted for six cancer types: colorectal, female breast, lung, pancreas, prostate, and thyroid. The observed numbers of incident cancer cases in 2020 were compared with the estimated numbers by calculating observed-to-expected (O/E) ratios. The expected numbers of incident cases were extrapolated using Joinpoint trend models. The authors report an O/E ratio <1.0 for major screening-eligible cancer sites, indicating fewer newly diagnosed cases than expected in 2020. The O/E ratios were lowest in April 2020. For every cancer site except pancreas, Asians/Pacific Islanders had the lowest O/E ratio of any race group. O/E ratios were lower for cases diagnosed at localized stages than for cases diagnosed at advanced stages. The current analysis provides strong evidence for declines in cancer diagnoses, relative to the expected numbers, between March and May of 2020. The declines correlate with reductions in pathology reports and are greater for cases diagnosed at in situ and localized stage, triggering concerns about potential poor cancer outcomes in the coming years, especially in Asians/Pacific Islanders. To help control the spread of coronavirus disease 2019 (COVID-19), health care organizations suspended nonessential medical procedures, including preventive cancer screening, during early 2020. Many individuals canceled or postponed cancer screening, potentially delaying cancer diagnosis. This study examines the impact of the COVID-19 pandemic on the number of newly diagnosed cancer cases in 2020 using first-submission, population-based cancer registry database. The monthly numbers of newly diagnosed cancer cases in 2020 were compared with the expected numbers based on past trends for six cancer sites. April 2020 had the sharpest decrease in cases compared with previous years, most likely because of the COVID-19 pandemic.

Replicate Testing of Clinical Endpoints Can Prevent No-Go Decisions for Beneficial Vaccines.

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation-or "dilution"-of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can substantially reduce efficacy dilution for assays with non-systematic (i.e., "random") errors. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. In clinical trials, where frequent longitudinal samples are needed to detect short-lived infections, this "confirmatory majority rule" strategy can prevent the accumulation of false positives from magnifying efficacy dilution. When widespread public health screening is used for viruses, such as SARS-CoV-2, that have non-differentiating features or may be asymptomatic, these strategies can also serve to reduce unneeded isolations caused by false positives.

Emotional responses to COVID-19 stressors increase information avoidance about an important unrelated health threat.

The COVID-19 pandemic, like other crises, has had direct and indirect impacts on individuals, many of which have been negative. While a large body of research has examined the impacts of COVID-19 on people's lives, there is little evidence about how COVID-19 affects decision-making broadly. Emotional responses to COVID-19-related stressors, such as illness and income loss, provide a pathway for these stressors to affect decision-making. In this study, we examine linkages between exposure to COVID-19-related stressors-focusing on temporally specific local case counts and loss of income due to the pandemic-and decisions to access information about antimicrobial resistance (AMR), another critically important health issue. COVID-19 constitutes a natural experiment in that people's exposure to stressors does not result from those individuals' current decisions. Using a nationally representative survey with 1223 respondents in December 2020, we linked the temporally specific COVID-19 cases and income loss experienced by participants to an increased likelihood of feeling hopeless. Higher feelings of hopelessness led to a higher probability of avoiding information about AMR. A mediation analysis confirms that emotional responses to COVID-19 stressors significantly increase information avoidance about an unrelated, but important health issue. Our results suggest that large-scale crises, like COVID-19 and climate change, may diminish action on other important health issues facing humanity.

A new time-varying coefficient regression approach for analyzing infectious disease data

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Projecting the future incidence and burden of dengue in Southeast Asia

The recent global expansion of dengue has been facilitated by changes in urbanisation, mobility, and climate. In this work, we project future changes in dengue incidence and case burden to 2099 under the latest climate change scenarios. We fit a statistical model to province-level monthly dengue case counts from eight countries across Southeast Asia, one of the worst affected regions. We project that dengue incidence will peak this century before declining to lower levels with large variations between and within countries. Our findings reveal that northern Thailand and Cambodia will show the biggest decreases and equatorial areas will show the biggest increases. The impact of climate change will be counterbalanced by income growth, with population growth having the biggest influence on increasing burden. These findings can be used for formulating mitigation and adaptation interventions to reduce the immediate growing impact of dengue virus in the region.

Capturing asymmetry in COVID-19 counts using an improved skewness measure for time series data

Capturing asymmetry among time series is an important area of research as it provides a range of information regarding the behaviour and distribution of the underlying series, which in turn proves to be useful for prediction. Classically, this can be achieved by modeling the skewness of the underlying series, usually using the standard measure. We present here an improved measure of skewness for time series which are integrated by a certain order, which is easy to calculate and proves to be advantageous over the existing one. We complement our methodology by implementing it to represent the heavy asymmetry among the daily COVID-19 case counts of several countries.•Improved skewness measure proves to be better than the usual skewness measure for time series data•This new measure is applied on COVID-19 daily counts to capture the asymmetry appropriately

On the local modeling of count data: multiscale geographically weighted Poisson regression

A recent addition to the suite of techniques for local statistical modeling is the implementation of the multiscale geographically weighted regression (MGWR), a multiscale extension to geographically weighted regression (GWR). Using a back-fitting algorithm, MGWR relaxes the restrictive assumption in GWR that all processes being modeled operate at the same spatial scale and allows the estimation of a unique indicator of scale, the bandwidth, for each process. However, the current MGWR framework is limited to use with continuous data making it unsuitable for modeling data that do not typically exhibit a Gaussian distribution. This study expands the application of the MGWR framework to scenarios involving discrete response outcomes (count data following a Poisson’s distribution). Use of this new MGWR Poisson regression (MGWPR) model is demonstrated with a simulated data set and then with COVID-19 case counts within New York City at the zip code level. The results from the simulated data underscore the superiority of the MGWPR model in effectively capturing spatial processes that influence count data patterns, particularly those operating across diverse spatial scales. For empirical data, the results reveal significant spatial variations in relationships between socio-ecological factors and COVID-19 cases – variations often missed by traditional ‘global’ models.

REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.

The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.

Rapidly developed, optimized, and applied wastewater surveillance system for real-time monitoring of low-incidence, high-impact MPOX outbreak.

Recent MPOX viral resurgences have mobilized public health agencies around the world. Recognizing the significant risk of MPOX outbreaks, large-scale human testing, and immunization campaigns have been initiated by local, national, and global public health authorities. Recently, traditional clinical surveillance campaigns for MPOX have been complemented with wastewater surveillance (WWS), building on the effectiveness of existing wastewater programs that were built to monitor SARS-CoV-2 and recently expanded to include influenza and respiratory syncytial virus surveillance in wastewaters. In the present study, we demonstrate and further support the finding that MPOX viral fragments agglomerate in the wastewater solids fraction. Furthermore, this study demonstrates that the current, most commonly used MPOX assays are equally effective at detecting low titers of MPOX viral signal in wastewaters. Finally, MPOX WWS is shown to be more effective at passively tracking outbreaks and/or resurgences of the disease than clinical testing alone in smaller communities with low human clinical case counts of MPOX.

Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa.

Country reported case counts suggested a slow spread of SARS-CoV-2 in the initial phase of the COVID-19 pandemic in Africa. Owing to inadequate public awareness, unestablished monitoring practices, limited testing and stigmas, there might exist extensive under-ascertainment of the true number of cases, especially at the beginning of the novel epidemic. We developed a compartmentalized epidemiological model to track the early epidemics in 54 African countries. Data on the reported cumulative number of cases and daily confirmed cases were used to fit the model for the time period with no or little massive national interventions yet in each country. We estimated that the mean basic reproduction number is 2.02 (s.d. 0.7), with a range between 1.12 (Zambia) and 3.64 (Nigeria). The mean overall report rate was estimated to be 5.37% (s.d. 5.71%), with the highest 30.41% in Libya and the lowest 0.02% in São Tomé and Príncipe. An average of 5.46% (s.d. 6.4%) of all infected cases were severe cases and 66.74% (s.d. 17.28%) were asymptomatic ones. The estimated low reporting rates in Africa suggested a clear need for improved reporting and surveillance systems in these countries.

Effect of storage condition and preservatives on the microbial, physicochemical and sensory quality of cucumber juice and carrots juice.

Fruit juices are prone to spoilage without the use of preservatives. This study evaluated the effect of preservatives on the microbial, physicochemical and sensory quality of cucumber and carrots juices. Carrot and cucumbers fruits were extracted, filtered and treated with garlic and ginger powder and sodium benzoate. Carrot and cucumber juices without preservatives served as control. The microbial qualities of the fruit juice were determined using standard methods. Total viable count for Cucumber and carrot juices stored at ambient and refrigerated temperature increased from day 3 to day 6. Cucumber and carrot Juices with Ginger + Garlic and sodium benzoate had the least microbial count in most cases. The microbial count for refrigerated cucumber and carrot juice were less than that of juice at ambient temperature on day 3 and day 6. The sensory quality of both juices deteriorated with a longer time of storage. The organisms isolated from both cucumber and carrot juices include Staphylococcus sp, Bacillus sp, E. coli, Klebsiella sp and Salmonella sp. Bacillus had the highest frequency of occurrence of 32%, while Salmonella sp 3.5% had the least occurrence. This study has shown that microorganisms are present in fresh fruit juices, microbial spoilage occurs with longer storage time but refrigeration slows down spoilage of fruit juices, chemical preservative benzoate and ginger + garlic in combination can slow down microbial spoilage. Also, the sensory quality of juices declines with longer storage but refrigeration improves sensory quality. However, preservation of fruit juice at ambient temperature for a long duration should be discouraged to reduce microbial contamination.