SARS-CoV-2 seroprevalence and associated factors among people living with HIV in Sierra Leone.

BACKGROUND: Human immunodeficiency virus (HIV) infection is an important risk factor for Coronavirus Disease 2019 (COVID-19), but data on the prevalence of COVID-19 among people living with HIV (PLWH) is limited in low-income countries. Our aim was to assess the seroprevalence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) specific antibodies and associated factors among PLWH in Sierra Leone. METHODS: We conducted a cross-sectional survey of PLWH aged 18 years or older in Sierra Leone between August 2022 and January 2023. Participants were tested for SARS-CoV-2 antibodies using a rapid SARS-CoV-2 antibody (immunoglobulin M/immunoglobulin G [IgG]) kits. Stepwise logistic regression was used to explore factors associated with SARS-CoV-2 antibody seroprevalence with a significance level of p < .05. RESULTS: In our study, 33.4% (1031/3085) participants had received a COVID-19 vaccine, and 75.7% were SARS-CoV-2 IgG positive. Higher IgG seroprevalence was observed in females (77.2% vs. 71.4%, p = .001), adults over 60 years (88.2%), those with suppressed HIV RNA (80.7% vs. 51.7%, p < .001), antiretroviral therapy (ART)-experienced individuals (77.9% vs. 44.6%, p < .001), and vaccinated participants (80.7% vs. 73.2%, p < .001). Patients 60 years or older had the highest odds of IgG seroprevalence (adjusted odds ratio [aOR] = 2.73, 95% CI = 1.68-4.65). Female sex (aOR = 1.28, 95%CI = 1.05-1.56), COVID-19 vaccination (aOR = 1.54, 95% CI = 1.27-1.86), and ART (aOR = 2.20, 95% CI = 1.56-3.11) increased the odds, whereas HIV RNA ≥ 1000 copies/mL (aOR = 0.32, 95% CI = 0.26-0.40) reduced the odds of IgG seroprevalence. CONCLUSIONS: We observed a high seroprevalence of SARS-CoV-2 antibody among PLWH in Sierra Leone. We recommend the introduction of targeted vaccination for PLWH with a high risk of severe COVID-19, especially those with an unsuppressed HIV viral load.

Prediction model for dengue fever based on interactive effects between multiple meteorological factors in Guangdong, China (2008-2016).

INTRODUCTION: In order to improve the prediction accuracy of dengue fever incidence, we constructed a prediction model with interactive effects between meteorological factors, based on weekly dengue fever cases in Guangdong, China from 2008 to 2016. METHODS: Dengue fever data were derived from statistical data from the China National Notifiable Infectious Disease Reporting Information System. Daily meteorological data were obtained from the China Integrated Meteorological Information Sharing System. The minimum temperature for transmission was identified using data fitting and the Ross-Macdonald model. Correlations and interactive effects were examined using Spearman's rank correlation and multivariate analysis of variance. A probit regression model to describe the incidence of dengue fever from 2008 to 2016 and forecast the 2017 incidence was constructed, based on key meteorological factors, interactive effects, mosquito-vector factors, and other important factors. RESULTS: We found the minimum temperature suitable for dengue transmission was ≥18°C, and as 97.91% of cases occurred when the minimum temperature was above 18 °C, the data were used for model training and construction. Epidemics of dengue are related to mean temperature, maximum/minimum and mean atmospheric pressure, and mean relative humidity. Moreover, interactions occur between mean temperature, minimum atmospheric pressure, and mean relative humidity. Our weekly probit regression prediction model is 0.72. Prediction of dengue cases for the first 41 weeks of 2017 exhibited goodness of fit of 0.60. CONCLUSION: Our model was accurate and timely, with consideration of interactive effects between meteorological factors.