Climate change (CC) is increasingly recognised as a critical driver in the emergence and re-emergence of infectious diseases. The relationship between CC and infectious diseases is complex and multifaceted, encompassing changes in temperature, precipitation patterns, and extreme weather events. This study describes the role of CC in the emergence and re-emergence of infectious diseases, emphasising zoonoses. It used a mixed methodology, including an initial literature contextualisation and a bibliometric analysis, to identify key thematic research areas related to CC and zoonotic diseases and show their connections. The research relied on the Scopus database for the identification of relevant source literature and focused the search query on publications in English. VOSviewer was used to discover clear thematic clusters that illustrate what research areas have been addressed in the literature and how they are interlinked. In addition, the research selected and analysed twelve literature-supported studies to investigate the relevance of the zoonoses involved in infectious disease emergence and re-emergence linked to CC impacts. Many pathogens and their vectors, such as mosquitoes, ticks, and rodents, are sensitive to temperature and moisture. CC can expand or shift the geographical distribution of these vectors, bringing diseases to new areas. Warmer temperatures may allow mosquitoes that transmit diseases like malaria and dengue fever to survive and reproduce in regions that were previously too cold. Also, extreme events such as floods, droughts, and hurricanes can lead to immediate increases in waterborne and vector-borne diseases (VBD) by facilitating the spread of pathogens. There is a need to better understand the connections between CC and zoonoses. To address the challenges posed by zoonoses linked to CC, international organizations like the WHO should coordinate a global response to provide clear guidance. Governments must integrate CC and zoonoses into national health policies, ensuring that health frameworks address these interconnected risks. Funding should be allocated for research on the root causes of CC and for strengthening defenses, particularly in developing countries with fragile health systems. Additionally, enhanced communication, education, and training for healthcare professionals about the links between CC and zoonoses are essential for raising awareness and promoting proactive measures.

Malaria remains one of the greatest issues in sub-Saharan Africa. This study aimed to identify the socio-economic and environmental determinants influencing the transmission of malaria and its incidence in West Africa. A systematic review was conducted using articles published from January 1989 to April 2025, within PubMed and Directory of Open Access Journals (DOAJ) databases. A total of 1145 articles related to our topic were found in the PubMed database, and 125 articles were identified in the DOAJ database. After inclusion and exclusion criteria, 68 articles were selected from both databases. The results indicate that among the environmental determinants, air temperature, rainfall, relative humidity, and vegetation are the most common environmental factors that predict malaria transmission. Moreover, education level, place of residence, housing structure, poverty, and quality of information are the key socio-economic determinants to consider in the prediction of malaria. These factors can be indicators for target programmes for the elimination and prevention of the infection of malaria in West Africa.

Climate change poses significant threats to African countries, with increasing temperatures, erratic rainfall patterns, and extreme weather events impacting ecosystems, agriculture, water resources, and human livelihoods. Artificial intelligence (AI) may offer valuable support in climate change adaptation efforts in the field of agriculture. However, considerable constraints also need to be addressed to maximise AI’s effectiveness. Against this background, this article outlines the potentials and risks of deploying AI in agriculture, as well as the need for clear regulatory frameworks for AI deployment, establishing guidelines that promote innovation while addressing ethical and legal concerns.

Vegetation health (VH) is a powerful characteristic for forecasting malaria incidence in regions where the disease is prevalent. This study aims to determine how vegetation health affects the prevalence of malaria and create seasonal weather forecasts using NOAA/AVHRR environmental satellite data that can be substituted for malaria epidemic forecasts. Weekly advanced very high-resolution radiometer (AVHRR) data were retrieved from the NOAA satellite website from 2009 to 2021. The monthly number of malaria cases was collected from the Ministry of Health of Benin from 2009 to 2021 and matched with AVHRR data. Pearson correlation was calculated to investigate the impact of vegetation health on malaria transmission. Ordinary least squares (OLS), support vector machine (SVM) and principal component regression (PCR) were applied to forecast the monthly number of cases of malaria in Northern Benin. A random sample of proposed models was used to assess accuracy and bias. Estimates place the annual percentage rise in malaria cases at 9.07% over 2009–2021 period. Moisture (VCI) for weeks 19–21 predicts 75% of the number of malaria cases in the month of the start of high mosquito activities. Soil temperature (TCI) and vegetation health index (VHI) predicted one month earlier than the start of mosquito activities through transmission, 78% of monthly malaria incidence. SVM model D is more effective than OLS model A in the prediction of malaria incidence in Northern Benin. These models are a very useful tool for stakeholders looking to lessen the impact of malaria in Benin.

Malaria is a significant vector-borne disease that exhibits high sensitivity to climatic variations within the West African region. In Benin, the effective prevention and mitigation of malaria pose considerable challenges, primarily due to the prevailing conditions of poverty and environmental adversities. This study endeavours to devise an advanced system for early detection and warning of malaria outbreaks in the northern part of Benin, employing monthly time series data pertaining to climatic variables. Monthly climate data were sourced from Meteorological Agency of Benin (METEO-Benin), alongside malaria incidence data procured from the database of the Benin Ministry of Health, that covered the timeframe of 2009–2021. To ascertain the influence of climatic variables on malaria incidence, principal component analysis was applied. Subsequently, an intelligent model for forecasting malaria outbreaks was developed using support vector machine (SVM) algorithm. The developed model for malaria outbreaks was then employed to establish an intelligent system for warning and forecasting malaria incidence on a monthly basis, utilising the Meteostat platform, an online weather data service provider, in conjunction with the Streamlit framework. This application exhibits responsiveness and compatibility across all web browsers. Relative humidity and maximal temperature significantly influence malaria incidence in the northern region of Benin. SVM regression algorithm forecasts 80% prediction rate for malaria incidence. Consequently, the intelligent malaria outbreak warning system was successfully devised, enabling the automatic and manual prediction of monthly malaria incidence rates within the districts of northern Benin. This system serves as a valuable tool for stakeholders and policymakers, facilitating proactive measures to curtail malaria transmission in Benin.

The manifold impacts of climate change are also seen in the field of health in most countries. It is particularly so in Africa, whose health systems are amongst the most fragile in the world. This Commentary showcases the degree of vulnerability of the health systems of African countries to climate change, and describes some measures aimed at increasing their resilience to climate shocks. African health systems face significant challenges due to climate change, necessitating a comprehensive approach to enhance resilience.