Community-based early warning systems involve community driven collection and analysis of information that enable warning messages to help a community to react to a hazard and reduce the resulting loss or harm. Most early warning systems are designed at the national or global level. Local communities’ capacity to predict weather conditions using indigenous knowledge has been demonstrated in studies focusing on climate change and agriculture in some African countries. This review was motivated by successes made in non-disease specific community-based early warning systems with a view to identify opportunities for developing similar systems for malaria. This article reviewed the existing community-based early warning systems documented in literature. The types of disasters that are addressed by these systems and the methodologies utilised in the development of the systems were identified. The review showed that most of the documented community-based early warning systems focus on natural disasters such as floods, drought, and landslides. Community-based early warning systems for human diseases are very few, even though such systems exist at national and regional and global levels. There is a clear gap in terms of community-based malaria early warning systems. The methodologies for the development of the community-based early warning systems reviewed mainly derive from the four elements of early warning systems; namely risk knowledge, monitoring, warning communication and response capability. The review indicated the need for the development of community based early warning systems for human diseases.Keywords: community; early warning; disaster; hazards

This paper discusses indigenous environmental indicators for the occurrence of malaria in ward 11, 15 and 18 of Gwanda district, Zimbabwe. The study was inspired by the successes of use of indigenous knowledge systems in community based early warning systems for natural disasters. To our knowledge, no study has examined the relationship between malaria epidemics and climatic factors in Gwanda district. The aim of the study was to determine the environmental indicators for the occurrence of malaria. Twenty eight key informants from the 3 wards were studied. Questionnaires, focus group discussions and PRA sessions were used to collect data. Content analysis was used to analyse the data. The local name for malaria was 'uqhuqho' literally meaning a fever. The disease is also called, "umkhuhlane wemiyane" and is derived from the association between malaria and mosquitoes. The findings of our study reveal that trends in malaria incidence are perceived to positively correlate with variations in both temperature and rainfall, although factors other than climate seem to play an important role too. Plant phenology and insects are the commonly used indicators in malaria prediction in the study villages. Other indicators for malaria prediction included the perceived noise emanating from mountains, referred to as "roaring of mountains" and certain behaviours exhibited by ostriches. The results of the present study highlight the importance of using climatic information in the analysis of malaria surveillance data, and this knowledge can be integrated into the conventional health system to develop a community based malaria forecasting system.

Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district. The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level. Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected. Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.

Malaria continues to be a major public health problem in Sub-Saharan Africa despite efforts that have been made to prevent and control the disease for many decades. The knowledge on prediction and occurrence of the disease that communities acquired over the years has not been seriously considered in control programmes. This article reports on studies that aimed to integrate indigenous knowledge systems (IKS) on malaria into the malaria control programme in Gwanda District, Zimbabwe. The studies were conducted over a 3-year period. Data were collected using participatory rural appraisals, key informant interviews, household interviews and workshops in three wards (11, 15 and 18) with the highest malaria incidence in Gwanda District. Disease livelihoods calendars produced by the community showed their knowledge on the relationship between malaria, temperature and rainfall, and thus an understanding of malaria as a hazard. Volunteer IKS experts willing to record the indigenous environmental indicators for the occurrence of malaria in the study area were identified by the communities. Indigenous environmental indicators for the occurrence of malaria were classified as insects, plant phenology, animals, weather and cosmological indicators. Plant phenology was emphasised more than the other indicators. A community-based malaria early warning system model was developed using the identified IKS indicators in two of the wards using the ward health team as an entry point to the health system. In the model, data on indicators were collected at the village level by IKS experts, analysed at ward level by IKS experts and health workers and relayed to the district health team.

Background: Zoonoses are a major threat to human health. Worldwide, rabies is responsible for approximately 59 000 deaths annually. In Zimbabwe, rabies is one of the top 5 priority diseases and it is notifiable. It is estimated that rabies causes 410 human deaths per year in the country. Murewa district recorded 938 dog bite cases and 4suspected rabies deaths between January 2017 and July 2018, overshooting the threshold of zero rabies cases. Of the 938dog bite cases reported in the district, 263 were reported in Ward 30 and these included all the 4suspected rabies deaths reported in the district. This necessitated a study to assess risk factors for contracting rabies in Ward 30, Murewa. Methodology/ Principal findings: A descriptive cross sectional survey was used for a retrospective analysis of a group of dog bite cases reported at Murewa Hospital, in Ward 30. Purposive sampling was used to select dog bite cases and snowball sampling was used to locate unvaccinated dogs and areas with jackal presence. The dog bite cases and relatives of rabies cases were interviewed using a piloted interviewer-administered questionnaire. Geographical Positioning System (GPS) coordinates of dog bite cases, vaccinated and unvaccinated dogs and jackal presence were collected using handheld GPS device. QGIS software was used to spatially analyse and map them. Dog owners were 10 times more likely to contract rabies compared to non-dog owners (RR = 10, 95% CI 1.06–93.7). Owners of unvaccinated dogs were 5 times more likely to contract rabies compared to owners of vaccinated dogs (RR = 5.01, 95% CI 0.53–47.31). Residents of the high density cluster (area with low cost houses and stand size of 300 square meters and below) were 64 times more likely to contract rabies compared to non-high density cluster residents (RR = 64.87, 95% CI 3.6039–1167.82). Participants who were not knowledgeable were 0.07 times more likely to contract rabies, compared to those who had knowledge about rabies. (RR = 0.07, 95% CI 0.004–1.25). Our study shows that the risk factors for contacting rabies included; low knowledge levels regarding rabies, dog ownership residing in the high density cluster, owning unvaccinated dogs and spatial overlap of jackal presence with unvaccinated dogs.

Despite extensive research on climate change impacts and adaptation strategies in Zimbabwe, their effectiveness from the perspective of smallholder farmers, particularly in the Gwayi catchment remains insufficiently understood. This study evaluated adaptation strategies in response to land use change and climate variability using a mixed-methods approach. Data were collected through a survey of 483 smallholder farmers, 10 focus group discussions (8–12 participants), and interviews with 25 key informants. Results show that despite farmers' awareness of improved practices, adaptation remains constrained by limited access to water, quality seeds, and technology (64%), financial constraints (46%), and knowledge gaps (19%). The most common adaptation strategies include soil water conservation (85%), drought-resistant crops (80%), early planting (55%), and diversification (49%), with soil water conservation and drought-tolerant crops being the most widely practiced and perceived as most effective. Regression analysis indicates that livestock ownership, participation in community climate-resilient programs, and recent drought experiences significantly (p < 0.05) influence both the choice and number of adaptation strategies adopted. These findings highlight the need for context-specific interventions to mitigate the impacts of climate and land use changes on food security. Strengthening resilience among smallholder farmers requires improved resource access, livestock support, community-based resilience programs, capacity building, financial aid, and enhanced drought preparedness. Implementing these measures will bolster adaptive capacity and promote sustainable agricultural practices in the face of climate uncertainty.