Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.

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The impact of COVID-19 has been felt across all sectors, from transportation, education, and public works to the daily operations of businesses like selling, retailing, and so forth. The business sector is among those badly affected, especially micro, small, and medium enterprises. The understanding of ground prevailing conditions is key in driving informed policies that would have meaningful impact on society with regard to overcoming the effects of the virus. Hence, this work is an attempt to report the real ground statistics and necessity of technological support with the goal of submitting a report of recommended policies to the concerned authorities. In this direction, this work presents the outcome of a survey conducted to assess the impact of COVID-19 on operations of micro, small, and medium enterprises and also to find out the interventions put in place around business environments so as to enforce adherence to COVID-19 health safety measures. The survey was part of a study to develop automated IoT-powered technological solutions that would help to enforce proper mask wearing in indoor environments and also observance of social distance requirements within business premises. A customized questionnaire was designed to capture data on various aspects central to the focus of the study. The study was carried out in the month of May 2021, in the Huye district of Rwanda. According to the survey findings, the major challenges faced by businesses due to COVID-19 include failure by clients to settle bills, reduced ability to expand investment, difficulty in accessing inputs domestically, lower domestic sales to consumers, and lower domestic sales to businesses. The results also reveal some positive points that most businesses were found to have: hand washing points, hand sanitizer dispensers, and mechanisms to enforce social distance between customer and customer and also customer and front desk worker. In a nutshell, this work is unique in terms of (1) the customized questionnaire about Rwanda’s needs, (2) field visit-based data collection for accurate data, and (3) including an assessment of the importance of technological intervention for better handling of public safety, especially in the MSME business sector.

A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

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