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SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.

Mutton is known as a source of fat. Too much fat can cause high blood pressure and hypertension which is more vulnerable to get infected by COVID-19. This study has a purpose to observe if there is a correlation between meat consumption and the number of COVID cases in the USA and China. This study is a quantitative type method with secondary data as a sample. The samples are divided into two groups, report of mutton consumption, and report of positive coronavirus cases. The result shows that the total import number of mutton does not have any negative impact on COVID cases. Rather, the total number of imports itself could reduce the number of active case. It can be concluded that the higher the total number of mutton imports, the lower of active case of COVID-19. [ABSTRACT FROM AUTHOR]

Background: Pakistan holds rank 8 th among the world countries in which high prevalence of tuberculosis (TB) is present. There is evidenced that up to 30% of close contacts of TB patients become infected and at least half of them progress to active disease. The aim of this study was to screen active contacts of sputum positive pulmonary TB (SS +ve PTB) among household contacts (HHCs) of TB patients in Karachi, Pakistan. Materials and Methods: Cross-sectional study, which was conducted at chest clinics of Karachi. Total 750 HHCs were screened out of which 135 diagnosed smear positive TB patients was registered as index cases (ICs) from July 2012 till March 2013. Logistic regression analysis was carried out to estimate the odds ratio (OR) of different associated factors. Results: Prevalence among males (6.1%) compared with female (5.6%). The crude OR for age less than 15 years was 0.28 (95% confidence interval (CI) 0.14-0.55). Eighty-eight HHCs (11.73%) were found to have SS +ve TB. Disease frequency was significantly higher in HHCs who were smokers (adjusted OR 36.41; 95% CI 12.07, 109.79), had age less than 12 years (adjusted OR 3.63; 95% CI 1.68, 7.86), and who lived in houses less than 80 square yards (adjusted OR 3.77; 95% CI 1.27, 6.05). Conclusion: The IC with positive sputum smear constitutes great risk for TB infection and disease in HHC. There is needed for contact tracing strategy in the high-risk population.

Background: Undiagnosed tuberculosis (TB) cases are the major challenge to TB control in Nigeria. An early warning outbreak recognition system (EWORS) is a system that is primarily used to detect infectious disease outbreaks; this system can be used as a case-based geospatial tool for the real-time identification of hot spot areas with clusters of TB patients. TB screening targeted at such hot spots should yield more TB cases than screening targeted at non-hot spots., Objective: We aimed to demonstrate the effectiveness of an EWORS for TB hot spot mapping as a tool for detecting areas with increased TB case yields in high TB-burden states of Nigeria., Methods: KNCV Tuberculosis Foundation Nigeria deployed an EWORS to 14 high-burden states in Nigeria. The system used an advanced surveillance mechanism to identify TB patients' residences in clusters, enabling it to predict areas with elevated disease spread (ie, hot spots) at the ward level. TB screening outreach using the World Health Organization 4-symptom screening method was conducted in 121 hot spot wards and 213 non-hot spot wards selected from the same communities. Presumptive cases identified were evaluated for TB using the GeneXpert instrument or chest X-ray. Confirmed TB cases from both areas were linked to treatment. Data from the hot spot and non-hot spot wards were analyzed retrospectively for this study., Results: During the 16-month intervention, a total of 1,962,042 persons (n=734,384, 37.4% male, n=1,227,658, 62.6% female) and 2,025,286 persons (n=701,103, 34.6% male, n=1,324,183, 65.4% female) participated in the community TB screening outreaches in the hot spot and non-hot spot areas, respectively. Presumptive cases among all patients screened were 268,264 (N=3,987,328, 6.7%) and confirmed TB cases were 22,618 (N=222,270, 10.1%). The number needed to screen to diagnose a TB case in the hot spot and non-hot spot areas was 146 and 193 per 10,000 people, respectively., Conclusions: Active TB case finding in EWORS-mapped hot spot areas yielded higher TB cases than the non-hot spot areas in the 14 high-burden states of Nigeria. With the application of EWORS, the precision of diagnosing TB among presumptive cases increased from 0.077 to 0.103, and the number of presumptive cases needed to diagnose a TB case decreased from 14.047 to 10.255 per 10,000 people., (©Chidubem Ogbudebe, Dohyo Jeong, Bethrand Odume, Ogoamaka Chukwuogo, Cyril Dim, Sani Useni, Okey Okuzu, Chenchita Malolan, Dohyeong Kim, Fiemu Nwariaku, Nkiru Nwokoye, Stephanie Gande, Debby Nongo, Rupert Eneogu, Temitayo Odusote, Salewa Oyelaran, Obioma Chijioke-Akaniro, Nrip Nihalani, Chukwuma Anyaike, Mustapha Gidado. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.02.2023.)