COVID-19 vaccine hesitancy among adults in Liberia, April-May 2021.

BACKGROUND: Vaccination is one of the most cost-effective public health interventions used to prevent diseases in susceptible populations. Despite the established efficacy of vaccines, there are many reasons people are hesitant about vaccination, and these reasons could be complex. This rapid survey estimated the prevalence of COVID-19 vaccine hesitancy and potentially contributing factors in Montserrado and Nimba counties in Liberia. METHODS: A cross-sectional study was conducted among adults living in Liberia. The relationship between vaccine non-acceptance and sociodemographic characteristics was examined using chi-square statistics. The variables with a p-value less than 0.2 at the bivariate analysis were modelled in a binary logistic regression at a 5% level of significance. The adjusted odds ratio and 95% confidence interval are reported. RESULTS: There were 877 participants in the study. Majority were 25-34 years of age (30.4%, 272/877), females (54.05%, 474/877), and Christians (85.2%, 747/877). Most of the participants were aware of the COVID-19 vaccine (75%, 656/877), single (41.4%, 363/877), self-employed (37.51%, 329/877), and live-in rural communities (56.1%, 492/877). Vaccine hesitancy was (29.1%, 255/877; 95% CI:26.2-32.2). Vaccine hesitancy was greater among adults living in urban areas (41%) compared to persons living in rural communities (59%) (aOR; 1.5, 95% CI: 1.1-2.1) and respondents aged 45-54 years (aOR:0.5; 95% CI: 0.2-0.9; p = 0.043) were 50% less likely to be hesitant to COVID-19 vaccination compared to those more than 55 years. The most common source of information was the media (53%, 492/877) and the main reason for being hesitant was a need for more information about the vaccine and its safety (84%, 215/255). CONCLUSIONS: The majority of study participants were aware of the COVID-19 vaccines and their most common source of information was the media (television, radio). Vaccine hesitancy was moderate. This could pose a challenge to efforts to control the spread of the COVID-19 pandemic. Therefore, the health authorities should provide more health education on the importance of vaccines and their safety to the populace.

COVID-19 reinfection in Liberia: Implication for improving disease surveillance.

COVID-19 remains a serious disruption to human health, social, and economic existence. Reinfection with the virus intensifies fears and raises more questions among countries, with few documented reports. This study investigated cases of COVID-19 reinfection using patients' laboratory test results between March 2020 and July 2021 in Liberia. Data obtained from Liberia's Ministry of Health COVID-19 surveillance was analyzed in Excel 365 and ArcGIS Pro 2.8.2. Results showed that with a median interval of 200 days (Range: 99-415), 13 out of 5,459 cases were identified and characterized as reinfection in three counties during the country's third wave of the outbreak. Eighty-six percent of the COVID-19 reinfection cases occurred in Montserrado County within high clusters, which accounted for over 80% of the randomly distributed cases in Liberia. More cases of reinfection occurred among international travelers within populations with high community transmissions. This study suggests the need for continued public education and surveillance to encourage longer-term COVID-19 preventive practices even after recovery.

Potential distribution of dominant malaria vector species in tropical region under climate change scenarios.

Risk assessment regarding the distribution of malaria vectors and environmental variables underpinning their distribution under changing climates is crucial towards malaria control and eradication. On this basis, we used Maximum Entropy (MaxEnt) Model to estimate the potential future distribution of major transmitters of malaria in Nigeria-Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis under low and high emissions scenarios. In the model, we used mosquito occurrence data sampled from 1900 to 2010 alongside land use and terrain variables, and bioclimatic variables for baseline climate 1960-1990 and future climates of 2050s (2041-2060) and 2070s (2061-2080) that follow RCP2.6 and RCP8.5 scenarios. The Anopheles gambiae species are projected to experience large shift in potential range and population with increased distribution density, higher under high emissions scenario (RCP8.5) and 2070s than low emission scenario (RCP2.6) and 2050s. Anopheles gambiae sensu stricto and Anopheles arabiensis are projected to have highest invasion with 47-70% and 10-14% percentage increase, respectively in Sahel and Sudan savannas within northern states in 2041-2080 under RCP8.5. Highest prevalence is predicted for Humid forest and Derived savanna in southern and North Central states in 2041-2080; 91-96% and 97-99% for Anopheles gambiae sensu stricto, and 67-71% and 72-75% for Anopheles arabiensis under RCP2.6 and RCP8.5, respectively. The higher magnitude of change in species prevalence predicted for the later part of the 21st century under high emission scenario, driven mainly by increasing and fluctuating temperature, alongside longer seasonal tropical rainfall accompanied by drier phases and inherent influence of rapid land use change, may lead to more significant increase in malaria burden when compared with other periods and scenarios during the century; especially in Humid forest, Derived savanna, Sahel and Sudan savannas.

Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt.

Malaria is a major infectious disease that still affects nearly half of the world's population. Information on spatial distribution of malaria vector species is needed to improve malaria control efforts. In this study we used Maximum Entropy Model (MaxEnt) to estimate the potential distribution of Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis in Nigeria. Species occurrence data collected during the period 1900-2010 was used together with 19 bioclimatic, landuse and terrain variables. Results show that these species are currently widespread across all ecological zones. Temperature fluctuation from mean diurnal temperature range, extreme temperature and precipitation conditions, high humidity in dry season from precipitation during warm months, and land use and land cover dynamics have the greatest influence on the current seasonal distribution of the Anopheles species. MaxEnt performed statistically significantly better than random with AUC approximately 0.7 for estimation of the Anopheles species environmental suitability, distribution and variable importance. This model result can contribute to surveillance efforts and control strategies for malaria eradication.