Background Adequate knowledge about COVID-19 in a population may be relevant in the fight to control its spread among the populace. Thus, the aim of this study was to assess the factors associated with real knowledge of COVID-19 among Ghanaians to promote effective dissemination of appropriate information aimed at containing the spread. Methods A cross-sectional online survey and computer assisted telephone interviews (CATI) was conducted among Ghanaians aged 18 years and above across the 260 districts of Ghana. The survey assessed the level of knowledge of COVID-19 and its associated factors and compared differences between perceived and real knowledge. One district health promotion officer per district was trained for the data collection. Participants were recruited via use of phone directories of both organized and non-organized local district groups. Phone calls were made to randomly selected phone contacts to schedule options for participation in the study. We used multivariable logistic regression to investigate the associated factors of COVID-19 knowledge among respondents. Results Of the 2,721 participants who completed the survey, the majority (99.3%) were aware of the existence of the COVID-19 outbreak, had good knowledge on infection prevention (87.0%) and rated their knowledge about COVID-19 as good (81.7%). Factors associated with COVID-19 knowledge were: age ≥56 years (aOR = 0.5; CI: 0.3–0.8; p = 0.002), tertiary education (aOR = 1.8; CI: 1.2–2.6; p = 0.003), residing in Greater Accra region (aOR = 2.0; CI: 1.1–3.6; p = 0.019), not infected with the novel coronavirus (aOR = 1.5; Cl: 1.0–2.1; p = 0.045), knowing an infected person (aOR = 3.5; CI = 1.5–7.9; p = 0.003), good practice of effective preventive measures (aOR = 1.2: Cl: 1.1–1.5: 0.008), not misinformed (aOR = 0.7; Cl: 0.5–0.9; 0.015), and perceiving spreading speed of the virus as slow (aOR = 0.7; Cl: 0.5–0.9; 0.007). Conclusion The study found good knowledge regarding COVID-19, control measures, and preventive strategies. The Ghana Health Service should continuously provide accurate information to educate the media and citizens to prevent misinformation, which is vital in stopping the spread of the COVID-19 virus.

Malaria is one of the leading causes of death in many developing nations throughout Africa, South America, and Asia. African countries are most prone to the deadly disease malaria and rising temperatures aid the spread of the disease (WHO 2012). More than 30 million women in Africa become pregnant in malaria endemic areas and are at risk of Plasmodium falciparum malaria infection compared with non-pregnant women. Malaria during pregnancy-related morbidity and mortality is most pronounced in sub-Saharan Africa, yet only a fraction of these women have access to effective anti-malaria interventions (Braun et al. 2015; Gutman and Slutsker 2017).

The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.

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There is an ongoing investigation on the transmission characteristics of COVID-19 with respect to country-based inflection points, nature of distribution and prediction of future trends. In this study, a new accelerated and delayed spread models for COVID-19 reported cases and deaths in Ghana were developed. Optimization techniques coupled with interpolations, least square and non-linear regression methods, to come out with an informed modeling strategy to predict the delayed spread for the case of Ghana were adopted. Derivative and tangent methods were also applied to determine inflection points for Ghana’s cases and death from COVID-19. The data used for the study covered the first 250 days of events and interventions of the pandemic in Ghana. It was realized that the distribution of the COVID-19 situation in Ghana followed an exponential distribution curve. A modification of the developed model to help optimize the error between observed and estimated values yielded an improvement in the prediction of the delayed phase. Our derived parameters revealed that transmission of the virus between phases depended on changes in the precautionary measures and peoples' behaviors. The study thus shows that Ghana passed her inflection point of reported cases on Sunday 19th July, 2020 and may currently be in the delayed phase characterized with a staggering trend where new infections similar in magnitude to previous infections may upsurge. The correlation between reported cases and deaths revealed linear dependence with positive deviation between accelerated and delayed phases. In conclusion, the study predicted the commencement of a new wave in Ghana after Wednesday October 28, 2020 with higher intensity than what was previously observed if timely impositions of interventions to minimize the effect of the second wave are not taken.