Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA.

The objective of this study is to compare the different methods which are effective in predicting data of the short-term effect of COVID-19 confirmed cases and DJI closed stock market in the US. Data for confirmed cases of COVID-19 has been obtained from Worldometer, the database of Johns Hopkins University and the US stock market data (DJI) was obtained from Yahoo Finance. The data starts from 20 January 2020 (first confirmed COVID-19 case the US) to 06 December 2020 and DJI data covers 21 January 2019 to 04 December 2020. COVID-19 data was tested for the period 30 November to 06 December and DJI from 25 November 2020 to 04 December. From the result, we find that the method SutteARIMA was found more suitable to calculate the daily forecasts of COVID-29 confirmed cases and DJI in the US and this method has been used in this study. For the evaluation of the prediction methods, the accuracy measure means absolute percentage error (MAPE) has been used. The MAPE value with the SutteARIMA of 0.56 and 0.60 for COVID-19 and DJI stock respectively was found to be smaller than the MAPE value with ARIMA method.

Impact of COVID-19 on cancer care in India: a cohort study.

BACKGROUND: The COVID-19 pandemic has disrupted health-care systems, leading to concerns about its subsequent impact on non-COVID disease conditions. The diagnosis and management of cancer is time sensitive and is likely to be substantially affected by these disruptions. We aimed to assess the impact of the COVID-19 pandemic on cancer care in India. METHODS: We did an ambidirectional cohort study at 41 cancer centres across India that were members of the National Cancer Grid of India to compare provision of oncology services between March 1 and May 31, 2020, with the same time period in 2019. We collected data on new patient registrations, number of patients visiting outpatient clinics, hospital admissions, day care admissions for chemotherapy, minor and major surgeries, patients accessing radiotherapy, diagnostic tests done (pathology reports, CT scans, MRI scans), and palliative care referrals. We also obtained estimates from participating centres on cancer screening, research, and educational activities (teaching of postgraduate students and trainees). We calculated proportional reductions in the provision of oncology services in 2020, compared with 2019. FINDINGS: Between March 1 and May 31, 2020, the number of new patients registered decreased from 112 270 to 51 760 (54% reduction), patients who had follow-up visits decreased from 634 745 to 340 984 (46% reduction), hospital admissions decreased from 88 801 to 56 885 (36% reduction), outpatient chemotherapy decreased from 173634 to 109 107 (37% reduction), the number of major surgeries decreased from 17 120 to 8677 (49% reduction), minor surgeries from 18 004 to 8630 (52% reduction), patients accessing radiotherapy from 51 142 to 39 365 (23% reduction), pathological diagnostic tests from 398 373 to 246 616 (38% reduction), number of radiological diagnostic tests from 93 449 to 53 560 (43% reduction), and palliative care referrals from 19 474 to 13 890 (29% reduction). These reductions were even more marked between April and May, 2020. Cancer screening was stopped completely or was functioning at less than 25% of usual capacity at more than 70% of centres during these months. Reductions in the provision of oncology services were higher for centres in tier 1 cities (larger cities) than tier 2 and 3 cities (smaller cities). INTERPRETATION: The COVID-19 pandemic has had considerable impact on the delivery of oncology services in India. The long-term impact of cessation of cancer screening and delayed hospital visits on cancer stage migration and outcomes are likely to be substantial. FUNDING: None. TRANSLATION: For the Hindi translation of the abstract see Supplementary Materials section.

COVID-19 pandemic and transmission factors: An empirical investigation of different countries.

The present work evaluates the impact of age, population density, total population, rural population, annual average temperature, basic sanitation facilities, and diabetes prevalence on the transmission of COVID-19. This research is an effort to identify the major predictors that have a significant impact on the number of COVID-19 cases per million population for 83 countries. The findings highlight that a population with a greater share of old people (aged above 65) shows a higher number of COVID-19 positive cases and a population with a lower median age has fewer cases. This can be explained in terms of higher co-morbidities and the lower general immunity in the older age group. The analysis restates the widely seen results that a higher median age and greater prevalence of co-morbidities leads to higher cases per million and lesser population density and interpersonal contact helps in containing the spread of the virus. The study finds foundation in the assertion that a higher temperature might lower the number of cases, or that temperature in general can affect the infectivity. The study suggests that better access to sanitation is a certain measure to contain the spread of the virus. The outcome of this study will be helpful in ascertaining the impact of these indicators in this pandemic, and help in policy formation and decision-making strategies to fight against it.