COVID-19 Pandemic: How Effective Are Preventive Control Measures and Is a Complete Lockdown Justified? A Comparison of Countries and States

For fighting the COVID-19 pandemic, countries used control measures of different severity, from “relaxed” to lockdown. Drastic lockdown measures are considered more effective but also have a negative impact on the economy. When comparing the financial value of lost lives to the losses of an economic disaster, the better option seems to be lockdown measures. We developed a new parameter, the effectiveness of control measures, calculated from the 2nd time derivative of daily case data, for 92 countries, states and provinces. We compared this parameter, and also the mortality during and after the effective phase, for countries with and without lockdowns measures by means of the Mann–Whitney test. We did not find any statistically significant difference in the effectiveness between countries with and without lockdowns (p > 0.76). There was also no significant difference in mortality during the effective phase (p > 0.1); however, a significant difference after the effective phase, with higher mortality for lockdown countries, was identified. The effectiveness correlated well with a parameter derived from the reproductive number (R2 = 0.9480). The average duration of the effective phase was 17.3 ± 10.5 days. The results indicated that lockdown measures are not necessarily superior to relaxed measures, which in turn are not necessarily a recipe for failure. Relaxed measures are, however, more economy-friendly.

SISTEM INFORMASI GEOGRAFIS SEBARAN PENYAKIT TUBERKULOSIS DI KECAMATAN PANINGGARAN KABUPATEN PEKALONGAN BERBASIS ANDROID

Tuberculosis is an infectious disease that can infect one person to another caused by the bacterium Mycobacterium tuberculosis. This disease is not only transmitted to people of productive age (15-45), but teenagers or children under 15 years can also be infected with the bacteria. Every year new cases are found in Indonesia, such as in the work area of the Paninggaran Health Center, Paninggaran District. Public Health Centers and Hospitals are health facilities for the community that have the facilities and infrastructure to determine how large the coverage of health figures for the community is. From this analysis, it can be found the obstacles that must be faced, namely how to provide information to the public about the spread of Tuberculosis in Paninggaran District. This is what underlies this research to create an Android-based Geographic Information System application for the distribution of tuberculosis in Paninggaran District with menus that are useful for the wider community and easy to use. The method used in this research is the Waterfall Method. The final result of this study is a "Geographical Information System for the Distribution of Tuberculosis in Paninggaran District" in the form of an android application for the community and a website for admins, namely Paninggaran Health Center officers, so that it can facilitate the distribution of information about Tuberculosis to the community and related health services, this application can also displays a map of the distribution of Tuberculosis disease, the location of where Tuberculosis patients live, the number of Tuberculosis case data and educational news about Tuberculosis disease.

Emergency Department Crowding is Harder to Solve: The Impact of COVID-19 of Longer Length of Stay

Objectives To determine the impact of the Coronavirus disease-2019 (COVID-19) pandemic on the length of stay (LOS) and prognosis of patients in the emergency department (ED). Methods A retrospective review of case data of patients in the ED during the early stages of the COVID-19 pandemic in the First Affiliated Hospital of Soochow University (January 15, 2020– January 14, 2021) was performed and compared with that during the pre-COVID-19 period (January 15, 2019 – January 14, 2020). Patient information including age, sex, length of stay, and death was collected. Wilcoxon Rank sum test was utilized to compare the difference in LOS between the two cohorts. Chi-Squared test was utilized to analyze the prognosis of patients. The LOS and prognosis in different departments (emergency internal medicine, emergency surgery, emergency neurology, and other departments) were further analyzed. Results Of the total 8278 patients, 4159 (50.24%) were ordered in the COVID-19 pandemic group and 4119 (49.76%) were ordered in the pre-COVID-19 group. The length of stay prolongs significantly in the COVID-19 group compared with that in the pre-COVID-19 group(13h vs 9.8h; p < 0.001). There was no significant difference in mortality between the two cohorts (4.8% VS 5.3%; p=0.341). Conclusion The COVID-19 pandemic was associated with a significant increase in the length of stay, which may lead to emergency department crowding. And the influence of the COVID-19 pandemic on patients in different emergency departments is different. There is no significant impact on the LOS of emergency neuropathy. Across departments, COVID-19 didn’t have a significant impact on the prognosis of ED patients.

A Method for Estimating the Number of Infections From the Reported Number of Deaths

At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.

Estimating the Local Burden of Disease During the First Wave of the COVID-19 Epidemic in England, Using Different Data Sources from Changing Surveillance Practices

Background The 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 - 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.

LESION OF THE RESPIRATORY TRACT IN CROHN’S DISEASE IN A CHILD OF 6 YEARS OLD

The article is devoted to rare extraintestinal manifestations of Crohn's disease (CD) in children, primarily to the respiratory tract. A review of the literature on this problem in children's practice is presented. On the example of clinical case, data on the classification, pathogenesis, clinical picture, diagnosis, differential diagnosis and treatment of CD with respiratory tract damage in a young child are summarized.

SARS-CoV-2 variant dynamics across US states show consistent differences in effective reproduction numbers

Accurately estimating relative transmission rates of SARS-CoV-2 Variant of Concern and Variant of Interest viruses remains a scientific and public health priority. Recent studies have used the sample proportions of different variants from sequence data to describe variant frequency dynamics and relative transmission rates, but frequencies alone cannot capture the rich epidemiological behavior of SARS-CoV-2. Here, we extend methods for inferring the effective reproduction number of an epidemic using confirmed case data to jointly estimate variant-specific effective reproduction numbers and frequencies of co-circulating variants using case data and genetic sequences across states in the US from January to October 2021. Our method can be used to infer structured relationships between effective reproduction numbers across time series allowing us to estimate fixed variant-specific growth advantages. We use this model to estimate the effective reproduction number of SARS-CoV-2 Variants of Concern and Variants of Interest in the United States and estimate consistent growth advantages of particular variants across different locations.

Surveillance of COVID-19 Using Geospatial Data: An Emergency Department Perspective

<b><i>Background:</i></b> The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. <b><i>Methods:</i></b> This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. <b><i>Results:</i></b> The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (<i>p</i> &#x3c; 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. <b><i>Conclusion:</i></b> This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.

Gambaran Epidemiologi Peningkatan Kasus Chikungunya di Desa Kajongan Kecamatan Bojongsari Kabupaten Purbalingga

Purbalingga is one of the chikungunya endemic districts which have cases increased until April in 2021 as many as 512 cases from the previous 116 cases in 2020. One of the areas of the increasing case occurred in Kajongan Village, Bojongsari Subdistrict. This study aims were to describe chikungunya cases, identify sources of transmission, and risk factors for increased chikungunya cases. A case-control design was performed in Kajongan Village in March 2021. The sample consisted of 36 cases and 36 controls. Cases are patients with suspected chikungunya, controls are people who are not chikungunya suspects who live close to the suspected case. Data was collected by blood specimen collection, larvae survey, interviews and examination using Reverse Transcriptase-Polymerase Chain Reaction. The results showed that the peak of case transmission occurred in the fourth week of February 2021. The main symptoms are fever and joint pain. Of the 21 blood samples examined, 19 (90.5%) tested positive for Chikungunya virus. Of the 9 pools Aedes sp. analyzed, 1 pool tested also positive Chikungunya virus. Chi-square analysis showed that the presence of larvae in container (p-value=0.03; OR=3.5; 95% CI=0.953-24.746) and mosquito nest eradication has not carried out routinely (p-value=0.04; OR=4.8; 95% CI=0.953-24.746) have been identified as a potential risk factor. The increasing of chikungunya cases in Kajongan Village was a local transmission supported by Chikungunya virus was detected in mosquitoes.