Methodologies to Associate COVID-19 Spreading Data to Space and Scale

Since the onset of the COVID-19 pandemic, the correlation between the spread of the SARS-Cov-2 virus and a number of epidemiological parameters has been a key tool for understanding the dynamics of its flow. This information has assisted local authorities in making policy decisions for the containment of its expansion. Several methods have been used including topographical data, artificial intelligence and machine learning data, and epidemiological tools to analyze factors facilitating the spread of epidemic at a local and global scale. The aim of this study is to use a new tool to assess and categorize the incoming epidemiological data regarding the spread of the disease as per population densities, spatial and topographical morphologies, social and financial activities, population densities and mobility between regions. These data will be appraised as risk factors in the spread of the disease on a local and a global scale.

SORMAS-Stats: A Novel Statistical Application for Real-time Estimation of Epidemiological Parameters of Communicable Diseases using Contact-tracing Data. (Preprint)

BACKGROUND The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in epidemic response. It consists of documentation, linkage and follow-up of cases, contacts, and events. To allow SORMAS users to visualise, compute key surveillance indicators and estimate epidemiological parameters from such a network data in real time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE The aim of this study is to describe the key visualisations, surveillance indicators and epidemiological parameters implemented in the SORMAS-Stats application, and illustrate the application of SORMAS-Stats to COVID-19 outbreak response. METHODS Based on literature review and user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number (Rt), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting a lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptoms onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. We applied the Markov Chain Monte Carlo approach and estimated Rt using the incidence data and the observed SI data, computed from the transmission network data. RESULTS Using COVID-19 contact tracing data of confirmed cases reported between July 31, and October 29, 2021 in Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes comprising 1.75% (1115/63570) events, 19.59% (12452/63570) case persons, and 78.66% (50003/63570) exposed persons, 1238 infector-infectee pairs, 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with best fit to the observed SI data was lognormal distribution with mean 4.32 days (95% CI, 4.10–4.53 days). We estimated the dispersion parameter, k of 21.11 (95% CI, 7.57–34.66) and a reproductive number, R of 0.9 (95% CI, 0.58–0.60). The weekly estimated Rt values ranged from 0.80 to 1.61. CONCLUSIONS We provide an application for real-time estimation of epidemiological parameters, which are essential for informing outbreak response strategies. These estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar applications by providing extensive visualisations and computation of surveillance indicators.

Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.

Analysis of the main epidemiological parameters of morbidity with tuberculosis of children and adolescents of the Ryazan region in 2010–2019

AIM: This study aimed to identify the main trends of the epidemiology of tuberculosis in children and adolescents in the Ryazan Region (RR) by analyzing the main epidemiological parameters of morbidity in 20102019. MATERIALS AND METHODS: The morbidity of children and adolescents with tuberculosis in the RR was subjected to retrospective epidemiological analysis. The data of the official and reporting documentation of the Ryazan Regional Clinical Antituberculosis Dispensary and the materials of the state reports of Territorial Administration of Rospotrebnadzor (On the Condition of SanitaryEpidemiological WellBeing of Population) in 20102019 were used. The main epidemiological parameters of morbidity due to tuberculosis were calculated using mathematical methods. RESULTS: The proportion of children and adolescents in the structure of morbidity due to tuberculosis in the RR in 20102019 decreased by 3.9 times and reached 6.09% in 2019. The morbidity caused by the active form of tuberculosis in children (014 years) and adolescents (1517 years) evidently declined by -20.7% and -11.5%, respectively. In children, respiratory tuberculosis predominated (55%100%), and the leading clinical form was tuberculosis in intrathoracic lymph nodes (77%). In adolescents, only pulmonary tuberculosis was identified, and focal tuberculosis was the predominating clinical form (43%). In the study period, the highest morbidity in children was recorded at the age of 714 years. No cases of mortality among children and adolescents with tuberculosis were recorded in 20102019. In 20182019, the primary infection and the risk of infection among children under 14 years of age increased from 1.3% (2018) to 1.8% (2019) and from 2.6% (2018) to 3.3% (2019), respectively. This result might indicate that morbidity due to tuberculosis increased. The majority of ill children and adolescents were identified among vaccinated ones, suggesting that the protective properties of the vaccine were insufficient (80%). CONCLUSION: The morbidity of children and adolescents with tuberculosis decreased, and this decrease directly associated with the general improvement in the status of tuberculosis in the country; in particular, tuberculosis foci in the RF decreased by 2.2 times [4]. In the study period, the morbidity of children (014 years) decreased by 3.5 and 2.1 times in the RR and RF, respectively. The morbidity of adolescents (1517 years) decreased by 3.1 and 2.2 times in the RR and RF, respectively. The mortality of children and adolescents in the RP was 0.0 per 100,000 population of the given age groups. In the RF, their mortalities reduced by 6.5 and 1.6 times, respectively [4].

Inference on COVID-19 Epidemiological Parameters Using Bayesian Survival Analysis

The need to provide accurate predictions in the evolution of the COVID-19 epidemic has motivated the development of different epidemiological models. These models require a careful calibration of their parameters to capture the dynamics of the phenomena and the uncertainty in the data. This work analyzes different parameters related to the personal evolution of COVID-19 (i.e., time of recovery, length of stay in hospital and delay in hospitalization). A Bayesian Survival Analysis is performed considering the age factor and period of the epidemic as fixed predictors to understand how these features influence the evolution of the epidemic. These results can be easily included in the epidemiological SIR model to make prediction results more stable.

RETROSPECTIVE STUDY OF CLINICAL & EPIDEMIOLOGICAL PARAMETERS OF PATIENTS UNDERGOING PERCUTANEOUS CORONARY INTERVENTION WITH THEIR FOLLOW-UP

Aim:To study clinical and epidemiological parameters of patients undergoing percutaneous coronary intervention (PCI) and to follow them up for understanding outcomes of procedure. Materials & methods:This is retrospective data analysis of 862 patients who underwent PCI from January 2016 to November 2017 Results: Out of 862 patients, 611 (70.88%) were male & 251 (29.12%) were female, with mean age being 55. 243 (28.19%) were diabetic, 470 (54.52%) were hypertensive, 158 (18.32%) patients were tobacco chewer, 215 (24.92%) were smokers & 111 (12.87%) were alcoholic. 636 (73.78%) patients had STEMI, 153 (17.74%) had NSTE-ACS, 61 (7.07%) had CSA.578 (67.05%) were SVD, 262 (30.39%) were DVD & 19 (2.20%) were TVD. Out of SVD, 350 (60.55%) patients had LAD involvement and among DVD patients, LAD & RCA were most commonly involved in 107 (40.83%) patients. On follow-up of mean 604.42 days (minimum 236 days, maximum 909 days), 2 (0.23%) episodes of subacute stent thrombosis occurred & 11 (1.27%) patients had ISR but no mortality was reported. Summary: The study shows affection of young population predominately and gender inequality suggesting primarily male disease. PCI is often sought in ACS and CSAis predominately treated medically. Thrombolysis still remains the rst treatment received by STEMI patients. SVD is the most common angiographic diagnosis with LAD predominately affected vessel. This real world-data on clopidogrel with aspirin as dual antiplatelet therapy and second generation stent shows negligible event of stent thrombosis & ISR. Limitation: Due to non-invasive follow-up, exact amount of stent restenosis can not be calculated. Impact on daily practice: This real world-data on clopidogrel with aspirin as dual anti-platelet therapy and second generation stent shows negligible event of stent thrombosis & ISR. This can help reduce cost burden on society and help better distribution of health budget.