ABSTRACT
Background: Nigeria recorded the first case of COVID 19 in February 2020 and imposed non pharmaceutical interventions including full-scale lockdown from April-May 2020. The lockdown and ensuing restrictions had an impact on routine HIV/AIDS services delivery among key population individuals (e.g. men who have sex with men, people who inject drugs, sex workers, transgender individuals and people in prisons). This study analyzed the impact of COVID-19 lockdown and restrictions on HIV/AIDS services including testing, identification of positives and linkage to treatment on a PEPFAR program in North Eastern Nigeria. Methods: A multi-centric retrospective study conducted in two states to assess the impact of COVID 19 on access to HIV services (testing, positives identified and linkage to HIV treatment). HIV services data from November 2019 to September 2020 was collected from source documents. We classified this period into four: pre-COVID (September 2019-March 2020), COVID lockdown (April-May 2020), COVID restrictions (June-July 2020) and relaxed restrictions (August-September 2020). A simple trend analysis of HIV services was done using a combination chart. Linear regression was conducted to understand the impact of COVID-19 on HIV services. The model for the Linear regression curve was plotted to compare the observed values with predicted (Linear) values. Results: We observed a sharp dip in HIV services during COVID lockdown and restriction (figure 1). The plots indicated a linear relationship between the month services were provided and HIV service outcome. The months services were conducted significantly predicted the number of testing (F(1, 9) = 20.689, p = .001), positives (F(1,9) = 15.857, p=0.003) and treatment (F(1,9) = 16.699, p=0.003) provided, accounting for 66.3%, 59.8% and 61.1% of the variation in number of HIV tests conducted, total HIV positive clients identified and clients placed on treatment respectively with adjusted R2 = 0.663, 0.598, and 0.611 for testing, positive and treatment respectively. The linear curve estimation showed that the actual HIV services outcome were below the projected estimated target in the months most affected by COVID 19 lockdown and restrictions. Conclusion: New pandemics can have negative effects on the control of other diseases such as HIV where health gains have been achieved in the past. Hence, a robust pandemic readiness plan must be developed for a possible second wave of COVID-19 to sustain the gains from several years of HIV intervention efforts.
ABSTRACT
The need for elucidating the effects of environmental factors in the determination of the novel corona virus (COVID-19) is very vital. This study is a methodological study to compare three different test models (1. Artificial neural networks (ANN), 2. Adaptive neuro fuzzy inference system (ANFIS), 3. A linear classical model (MLR)) used to determine the relationship between COVID-19 spread and environmental factors (temperature, humidity and wind). These data were obtained from the studies (Pirouz, Haghshenas, Haghshenas, & Piro, 2020) with confirmed COVID-19 patients in Wuhan, China, using temperature, humidity and wind as the independent variables. The measured and the predicted results were checked based on three different performance indices;Root mean square error (RMSE), determination coefficient (R2) and correlation coefficient (R). The results showed that ANFIS and ANN are more promising over the classical MLR models having an average R-values of 0.90 in both calibration and verification stages. The findings indicated that ANFIS outperformed MLR and ANN. In addition, their performance skills boosted up to 25% and 9% respectively based on the determination coefficient for the prediction of confirmed COVID-19 cases in Wuhan city of China. Overall, the results depict the reliability and ability of AI-based models (ANFIS and ANN) for the simulation of COVID-19 using the effects of various environmental variables.