The COVID-19 pandemic has been slow to arrive in the world's poorest countries, especially in Africa. There are good reasons to believe that the consequences for the continent could be worse than anywhere else. 1-3 The weaknesses of some governments, healthcare systems, and economies, plus armed conflict, are factors that the virus can and will exploit. A recent British Broadcasting Corporation article noted that there are 10 African countries that have no ventilators, nine have < 1 per 1 million people, and most of the others have too few to serve their populations in an outbreak of U.S. proportions. 2 African countries need help but are not all helpless. To adequately preview the impact of COVID-19 on the continent, however, both weaknesses and strengths must be considered. The Africa of 2020 is not the Africa of 1960 or even 2014. Africa is a continent of 54 countries, with a range of climatic, cultural, demographic, and economic conditions that contrast them with more developed regions and with each other (Table 1). The country-to-country effects of COVID-19 could be quite different, and there are resources that may help produce better than expected outcomes. Demographics: The population structure in Africa is relatively protected against serious COVID-19. The major risk factors for a serious disease are older age, diabetes, heart disease, and obesity. 4,5 Africa is young. Only 2-5% of Africans are older than 65 years, and the median age is 19 years. Rates of chronic diseases, although rising, are currently lower than those of any other continent (Table 1). Sub-Saharan Africa is still mostly rural (59%). This is rapidly changing, but rural areas are less densely populated, and mobility in many areas is limited by poor road and air transportation. Thus, some physical distancing is built into the structure of many countries. On the other hand, urban areas are densely packed, and poverty can be extreme. A major portion of the population will not eat if they must shelter at home. That home in Nairobi's Kibera slum, for example, is in a community with 300,000 people per square kilometer (New York City has 10,000 per square kilometer). These are places where physical distancing has no meaning. Healthcare systems: In comparison with the United States, Italy, and China, healthcare systems are fragile in most countries on the continent (Table 1). Scarcity of healthcare workers is one of the weakest points across all African countries. South Africa and Ghana, however, have more than 200 hospital beds per 100,000 populations, which compares favorably with the United States. Not reflected in the numbers are recent initiatives in healthcare management by

Cross-sectional study was conducted using panel blood films and questioner to assess detection & identification performance of laboratory professionals' and identify factors affecting the performance of malaria microscopic diagnosis. Study participants had 91.7% (95% CI: 89.96 -93.44) agreement for detection of malaria parasites, 67.63% (95% CI: 64.91 -70.35) species identification agreement for Plasmodium falciparum, 5.08% false positive and 21.04% false negative results. Correct species identification percentage for Plasmodium falciparum were 60.9% (510), Plasmodium vivax 59.17% (371) and Mixed (Plasmodium falciparum and Plasmodium vivax) 25% (53) were also identified in the study. In addition, sensitivity 94.69% (95% CI: 93.02 -96.36) and specificity of 79.71 (95% CI: 75.22 -84.2) were calculated from panel blood film results. The most frequent type of misdiagnosis was 85(40.09%) mixed BFs diagnosed as Plasmodium vivax, 67 (31.6%) mixed BFs as Plasmodium falciparum and 218(26%) Plasmodium falciparum BFs as Plasmodium vivax. Moreover, only 18(8.5%) laboratory professionals were participated in external quality assessment. From multiple logistic regression analysis training was the major factor for species identification percent agreement performance improvement of laboratory professionals. It showed statistical significance with p-value < 0.05 and untrained laboratory professionals were 64% less likely to perform ≥ 85% agreement of species identification. Training of laboratory professionals on malaria microscopic diagnosis help to improve the accuracy and reliability of reported results. This will help to provide the right and recommended medication and patient management.

Background In Ethiopia, despite improvements in coverage and access, utilization of long-lasting insecticidal nets (LLINs) remains a challenge. Different household-level factors have been identified as associated with LLIN use. However, the contribution of LLIN physical integrity to their utilization is not well investigated and documented. This study aimed to assess the association between the physical integrity of LLINs and their use. Methods This study employed a nested case-control design using secondary data from the Ethiopian LLIN durability monitoring study conducted from May 2015 to June 2018. LLINs not used the night before the survey were identified as cases, while those used the previous night were categorized as controls. The physical integrity of LLINs was classified as no holes, good, acceptable, and torn using the proportionate hole index (pHI). A Generalized Estimating Equation (GEE) model was used to assess and quantify the association between LLIN physical integrity and use. The model specifications included binomial probabilistic distribution, logit link, exchangeable correlation matrix structure, and robust standard errors. The factors included in the model were selected first by fitting binary regression, and then by including all factors that showed statistical significance at P-value less than 0.25 and conceptually relevant variables into the multivariate regression model. Results A total of 5277 observations fulfilled the inclusion criteria. Out of these 1767 observations were cases while the remaining 3510 were controls. LLINs that were in torn physical condition had higher odds (AOR [95% CI] = 1.76 [1.41, 2.19]) of not being used compared to LLINs with no holes. Other factors that showed significant association included the age of the LLIN, sleeping place type, washing status of LLINs, perceptions towards net care and repair, LLIN to people ratio, economic status, and study site. Conclusion and recommendation LLINs that have some level of physical damage have a relatively higher likelihood of not being used. Community members need to be educated about proper care and prevention of LLIN damage to delay the development of holes as long as possible and use available LLINs regularly.

Background The functional survival time of long-lasting insecticidal nets (LLINs), which varies across different field contexts, is critical for the successful prevention of malaria transmission. However, there is limited data on LLIN durability in field settings in Ethiopia. Methods A three-year longitudinal study was conducted to monitor attrition, physical integrity, and bio-efficacy and residual chemical concentration of LLINs in four regions in Ethiopia. World Health Organization (WHO) guidelines were used to determine sample size, measure physical integrity, and calculate attrition rates, and functional survival time. Yearly bio-efficacy testing was done on randomly selected LLINs. An excel tool developed by vector works project was used to calculate the median functional survival time of the LLINs. Predictors of functional survival were identified by fitting binary and multivariate cox proportional hazards model. Results A total of 3,396 LLINs were included in the analysis. A total of 3,396 LLINs were included in the analysis. By the end of 36 months, the proportion of LLINs functionally surviving was 12.9% [95% confidence interval (CI) 10.5, 15.6], the rates of attrition due to physical damage and repurposing were 48.8% [95% confidence interval (CI) 45.0, 52.6] and 13.8% [95% confidence interval (CI) 11.6, 14.6], respectively. The estimated median functional survival time was 19 months (95%CI 17, 21). Factors associated with shorter functional survival time include being in a low malaria transmission setting [Adjusted Hazards Ratio (AHR) (95%CI) 1.77 (1.22, 2.55)], rural locations [AHR (95%CI) 1.83 (1.17, 2.84)], and in a room where cooking occurs [AHR (95%CI) 1.28 (1.05, 1.55)]. Bioassay tests revealed that 95.3% (95%CI 86.4, 98.5) of the LLINs met the WHO criteria of bio-efficacy after 24 months of distribution. Conclusion The LLIN survival time was shorter than the expected three years due to high attrition rates and rapid loss of physical integrity. National malaria programmes may consider, procuring more durable LLINs, educating communities on how to prevent damage of LLINs, and revising the current three-year LLIN distribution schedule to ensure sufficient protection is provided by LLINs against malaria transmission. While this paper contributes to the understanding of determinants impacting functional survival, further research is needed to understand factors for the rapid attrition rates and loss of physical integrity of LLINs in field settings.

Background The use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources. Methods A GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions. Results A case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as ‘extremely high’ or ‘high’ priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions. Conclusion The authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.