We evaluated the impact of man-made conflict events and climate change impact in guiding evidence-based community “One Health” epidemiology and emergency response practice against re-/emerging epidemics. Increasing evidence of emerging and re-emerging zoonotic diseases including recent Lassa fever outbreaks in almost 20 states in Nigeria led to 101 deaths and 175 suspected and confirmed cases since August 2015. Of the 75 laboratory confirmed cases, 90 deaths occurred representing 120% laboratory-confirmed case fatality. The outbreak has been imported into neighbouring country such as Benin, where 23 deaths out of 68 cases has also been reported. This study assesses the current trends in re-emerging Lassa fever outbreak in understanding spatio-geographical reservoir(s), risk factors pattern and Lassa virus incidence mapping, inherent gaps and raising challenges in health systems. It is shown that Lassa fever peak endemicity incidence and prevalence overlap the dry season (within January to March) and reduced during the wet season (of May to November) annually in Sierra Leone, Senegal to Eastern Nigeria. We documented a scarcity of consistent data on rodent (reservoirs)-linked Lassa fever outbreak, weak culturally and socio-behavioural effective prevention and control measures integration, weak or limited community knowledge and awareness to inadequate preparedness capacity and access to affordable case management in affected countries. Hence, robust sub/regional leadership commitment and investment in Lassa fever is urgently needed in building integrated and effective community “One Health” surveillance and rapid response approach practice coupled with pest management and phytosanitation measures against Lassa fever epidemic. This offers new opportunities in understanding human-animal interactions in strengthening Lassa fever outbreak early detection and surveillance, warning alerts and rapid response implementation in vulnerable settings. Leveraging on Africa CDC centre, advances in cloud-sourcing and social media tools and solutions is core in developing and integrating evidence-based and timely risk communication, and reporting systems in improving contextual community-based immunization and control decision making policy to effectively defeat Lassa fever outbreak and other emerging pandemics public health emergencies in Africa and worldwide.

Introduction: Geographical Information System (GIS) has proven to be very useful for large scale mapping of ecosystems, land use and cover, disease prevalence, risk mapping and forecasting. GIS establish relationship or link between vector borne diseases and associated environmental factors thereby providing explanation for spatial distribution pattern, possible causes of diseases outbreak and implications on the community. Aims and Objectives: Our approach in this study was to define and identify areas and places that are exposed to Malaria risk through proximity analysis and to compare geospatial risk with laboratory diagnosed malaria epidemiology. Methodology: Garmin GPS was used to capture the geographic coordinates of six (6) selected settlements and overlaid with georeferenced and processed satellite images in the study area. GIS modeling was performed on risk factors using weighted overlay technique to produce malaria risk map. A total of One hundred and thirty-five (135) vulnerable individuals were diagnosed for Malaria with light Olympus microscope and rapid diagnostic kit (RDT). Data were entered and analyzed using R-Package for Statistical Computing and Graphics. Results: Proximity to malaria risk follows relatively the order Apodu > Central Malete > Elemere > KWASU Campus > Gbugudu. Apodu being the largest place with proximity to malaria risk, within 500 m radius. The risk index increases as one move away from the center of the settlement. The possible explanation for this high risk could be the presence of pond / lake in Apodu. This is a good breeding site for mosquito couple with dense vegetation as one move away from the centre of the settlements. Unlike Apodu, Gbugudu was at medium risk at 100 m buffer (60%) but the risk index decreases as one move away from the settlement centre. The absence of thick vegetation and presence of numerous open farms and partly cultivated farmlands on the eastern part could have been responsible for reduction in risk index. Dense vegetation and ponds were observed within Apodu, while Central Malete was built up with dense vegetation are possible reasons for the high-risk index, while settlements within 1 km radius around KWASU campus recorded lower risk index possibly due to low vegetation. The geospatial malaria risk analysis correlates with the laboratory-based test results. RDT kits and light microscopy results showed Apodu having the highest malaria prevalence with 46% and 58.7% followed by Elemere 41% and 30.3% respectively. When calculating prevalence by aggregating results across all communities, Apodu still had the highest malaria prevalence for the whole region. RDT and light microscopy results combined for all communities had Apodu with malaria prevalence of 21.48% and 27.4% followed by Elemere with 11.85% and 12.5% respectively. Gbugudu had the least malaria prevalence within the region with 3.7% and 7.4% respectively. Discussion and Conclusion: Findings of this study showed dense vegetation and ponds within Apodu, Elemere and Central Malete served as good breeding site for mosquitoes and were responsible for the high-risk index at these areas. Settlements within 1 km radius around KWASU campus recorded lower index possibly due to low vegetation. Results from this study indicate that the degree of malaria parasitaemia in the three major settlements correlates directly with the remote sensing data.

In sub-Saharan Africa, climate change presents considerable threats to the health and well-being of women of reproductive age and children under 5 years. Some documented impacts include pregnancy complications, irregular breastfeeding, malnutrition, stunting, wasting, and diminished access to healthcare. While significant scholarly efforts have explored these outcomes, regional understanding of impacts remains vague. A systematic search of peer-reviewed and grey literature was conducted across four databases and search engines: Web of Science, PubMed, Google Scholar, and Scopus. Inclusion criteria required documents to be focused on sub-Saharan Africa, address the intersection of climate change and health, and report specific health outcomes. Selected studies were thematically analysed to identify and synthesise evidence-based mitigation strategies. 51 documents met the inclusion criteria. Climatic impacts including excessive or low rainfall, flooding, temperature fluctuations, heat waves, droughts, and tropical cyclones were found to be associated with various health outcomes. Among mothers, these included miscarriage, postpartum haemorrhage, and hypertensive disorders, while in children, impacts were linked to preterm birth, stillbirth, neonatal mortality, low birth weight, and malnutrition. Furthermore, climate change was found to impact access to healthcare across both populations in sub-Saharan Africa. Our findings underscore the need for regional integration in the planning and implementation of interventions to mitigate climate change impacts on maternal and child health. While acknowledging context-specific health outcomes due to differences in climate zones, this review offers a roadmap for prioritizing vulnerable populations in public health policy design and decision-making processes.

Malnutrition in children aged 0-5 years is a significant public health concern in many societies and is influenced by various sociodemographic factors that, when understood, may improve intervention planning and results. Background. This study aimed to identify the sociodemographic determinants of malnutrition in rural and urban areas of Abia State, Nigeria. Methods. A cross-sectional observational study involving 842 children (aged 6-59 months) and their caregivers was conducted across selected rural and urban areas in Abia State, Nigeria. Data collected through semistructured questionnaires focused on sociodemographic information as well as anthropometric measurements such as weight, height, and mid-upper arm circumference (MUAC). The dietary diversity score (DDS) was also calculated for each child via a questionnaire that recorded the consumption of various food groups over a 7-day period. Chi-square tests were adopted for categorical variables, whereas binomial logistic regression was used to identify determinants of malnutrition and weight classification. Results. Marked sociodemographic differences were found between rural and urban caregivers, with urban caregivers being more likely to have higher knowledge of nutrition (p < 0.01) and education (p < 0.01). Malnutrition, indicated by global acute malnutrition (GAM), was significantly higher in rural areas (53.5%) than in urban areas (46.5%) (χ2 = 5.353, p = 0.021). Logistic regression revealed that children of caregivers in rural areas were more likely to have an abnormal BMI-for-age (AOR: 1.407, 95% CI: 1.001–1.978, p = 0.049), whereas children of caregivers in urban areas were less likely to be malnourished (COR: 0.565, 95% CI: 0.347–0.921, p = 0.022). Conclusion. Implementation scientists should focus on enhancing caregiver education, particularly in rural areas, to promote awareness of diets, thereby reducing malnutrition.