Outbreaks of different viral etiologies amidst COVID-19 pandemic

The COVID-19 pandemic has severely affected public health system and surveillance of other communicable diseases across the globe. The lockdown, travel constraints and COVID phobia turned down the number of people with illness visiting to the clinics or hospitals. Besides this, the heavy workload of SARS-CoV-2 diagnosis has led to the reduction in differential diagnosis of other diseases. Consequently, it added to the underlying burden of many diseases which remained under-diagnosed. Amidst the pandemic, the rise of emerging and re-emerging infectious diseases was observed worldwide and reported to the World Health Organization i.e., Crimean Congo Hemorrhagic Fever (2022, Iraq;2021 India), Nipah virus (2021, India), Zika virus (2021, India), and H5N1 influenza (2021, India), Monkeypox (2022, multicountry outbreak), Ebola virus disease (2022, DRC, Uganda;2021, DRC, Guinea;2020, DRC), Marburg (2022, Ghana;2021, Guinea), Yellow fever (2022, Uganda, Kenya, West and Central Africa;2021, Ghana, Venezuela, Nigeria;2020, Senegal, Guinea, Nigeria, Gabon;2020, Ethiopia, Sudan, Uganda), Dengue (2022, Nepal, Pakistan, Sao Tome, Temor-Leste;2021, Pakistan), Middle east respiratory syndrome coronavirus (2022, Oman, Qatar;2021, Saudi Arabia, UAE;2020, Saudi Arabia, UAE), Rift valley fever (2021, Kenya;2020, Mauritania), wild poliovirus type 1 (2022, Mozambique), Lassa fever (2022, Guinea, Togo, Nigeria;2020, Nigeria), Avian Influenza (H3N8) (2022, China), Avian Influenza (H5N1) (2022, USA), H10N3 influenza (2021, China), Hepatitis E virus (2022, Sudan), Measles (2022, Malawi, Afghanistan;2020, Burundi, Mexico), Mayaro virus disease (2020, French Guiana), Oropouche virus disease (2020, French Guiana). All these diseases were associated with high morbidity and burdened the public health system during the COVID-19 pandemic. During this critical public health menace, majority of the laboratory workforce was mobilized to the SARS-CoV-2 diagnosis. This has limited the surveillance efforts that likely led to under diagnosis and under-detection of many infectious pathogens. Lockdowns and travel limitations also put a hold on human and animal surveillance studies to assess the prevalence of these zoonotic viruses. In addition, lack of supplies and laboratory personnel and an overburdened workforce negatively impacted differential diagnosis of the diseases. This is especially critical given the common symptoms between COVID-19 and other pathogens causing respiratory illnesses. Additionally, the vaccination programs against various vaccine preventable diseases were also hampered which might have added to the disease burden. Despite these challenges, the world is better prepared to detect and respond to emerging/re-emerging pathogens. India now has more than 3000 COVID-19 diagnostic laboratories and an enhanced hospital infrastructure. In addition, mobile BSL-3 facilities are being validated for onsite sampling and testing in remote areas during outbreak situations and surveillance activities. This will undoubtedly be valuable as the COVID-19 pandemic evolves as well as during future outbreaks and epidemics. In conclusion, an increase in the emergence and re-emergence of viruses demonstrates that other infectious diseases have been neglected during the COVID-19 pandemic. Lessons learned from the infrastructure strengthening, collaborations with multiple stakeholders, increased laboratory and manufacturing capacity, large-scale COVID-19 surveillance, extensive network for laboratory diagnosis, and intervention strategies can be implemented to provide quick, concerted responses against the future threats associated with other zoonotic pathogens.

Relationship between Stringency Index and Covid-19 confirmed cases in East Africa

Introduction/ Background: Governments worldwide have established measures to alleviate the spread of Coronavirus disease (COVID-19). To quantify the strictness of governments' response to COVID-19, the Oxford COVID-19 Government Response Tracker developed a Global Stringency Index. Our research aims to examine the relationship between the Stringency Index and COVID-19 cases in East Africa. Methods: To assess the impact of the non-pharmaceutical interventions (also known as lockdown measures) taken by different African countries on the spread of COVID-19, our research used publicly available confirmed COVID-19 cases and Stringency Index data from Our World in Data online platform. We focused our analysis on the correlation between the Stringency Index and confirmed COVID-19 cases in East Africa, particularly Rwanda, Kenya, Uganda, Tanzania, and Burundi. Furthermore, we analyzed Stringency index and COVID-19 confirmed cases timeseries of Rwanda. Results: Our preliminary results show that the correlation value between daily COVID-19 confirmed cases and Stringency index was equal to -0.57, -0.36, -0.15, 0.14, 0.2 in Rwanda, Kenya, Uganda, Tanzania, and Burundi respectively. While we plotted the Stringency Index and COVID-19 confirmed cases time series of Rwanda, we found that from June 1st, 2021 to September 26th, 2021 the Rwandan stringency index was constant at 54.63 while the cases were decreasing after the third wave. Impact: Since the stringency index examine how well different governments worldwide respond to COVID-19, our research aims to expand on assessing the contextual factors that make a difference in the African setting and establishing their efficacy and broad applicability to other African countries. Conclusion: The inverse relationship observed between the Stringency Index and confirmed COVID-19 cases suggests that the stricter the measures established by governments the fewer there were new infections of COVID-19 and vice versa. Our research underscores the need to contextualize stringency measures within the African setting.

Highlands Malaria is Common but HIV very rare in Rural Areas of Burundi 2,000 Meters above Sea Level (Short communication)

Highlands malaria is defined as falciparum parasitaemia at 1,500meters above sea level nowadays above 2,000 meters. Here we describe the relatively high proportion among outpatients in rural Burundi with a low density of population and travel mobility leading to minimal occurrence of HIV and TB.

Monetary Policy, External Shocks and Economic Growth Dynamics in East Africa: An S-VAR Model

Resulting from the incessant political and economic uncertainty that bedevils the EAC region in the recent past, the various governments have used monetary policy changes in response to shocks from macroeconomic variables. However, the available literature shows a non-agreement by scholars as far as the dynamics in monetary policy, external shocks and macroeconomic activity connections are concerned, for both country-by-country analyses and regional assessments. This article widens the frontiers of knowledge about how the dynamics of monetary policy, external shocks and macroeconomic performance interact within the EAC economic region. We adopted the S-VAR method because of its contemporary nature as far as a transmission of monetary policy approach is concerned. The interconnectivity among the countries of EAC is an indication that any shock to the price of commodities (non-oil commodities) has significant implication on the exchange rate, which will be channelled through the supply of money and monetary policy to the GDP. The need to diversify the productive and export base of member countries, compared to the continuous dependence on one or a few products as the major source of income, is hereby advocated.

Forecasting Daily Covid-19 Cases in Burundi Using a Machine Learning Algorithm

In this study, the ANN approach was applied to analyze daily new COVID-19 cases. The employed daily data covers the period 1 January 2020 to December 2020 and the out-of-sample period ranges over the period January 2021 to 31May 2021. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting daily new COVID-19 cases in Burundi. The results of the study indicate that daily COVID-19 cases are likely to be between 0-10 cases per day over the out of sample period. Therefore the government of Burundi must continue enforcing the implementation of WHO recommendations on the prevention and control of COVID-19.

Malaria and COVID-19: A double battle for Burundi.

Malaria has become a serious public health concern in Burundi. An outbreak that has the potential to evolve into an epidemic has eradicated nearly as many individuals as the Ebola crisis within the adjacent Democratic Republic of the Congo. The government's delay to announce a national crisis, increased breeding sites as a result of flooding, and the presence of multi-drug resistant malaria have exacerbated the burden. With a concurrent COVID-19 pandemic, economic complications, and overlap of symptoms between both diseases, these challenges are complex, but not unfamiliar. Organizations such as Médecins Sans Frontières have carried out spraying campaigns, and the government is actively mitigating efforts to handle the pandemic. That being said, there is still a need to enhance preventive measures such as increasing technological capacity and epidemiological surveillance to better withstand challenges.

A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy.

BACKGROUND: Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent's poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus's impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination. OBJECTIVE: The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA. METHODS: We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general. CONCLUSIONS: Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21955.

Understanding the influence of the COVID-19 pandemic on hospital-based mortality in Burundi: a cross-sectional study comparing two time periods.

This study used hospital records from two time periods to understand the implication of COVID-19 on hospital-based deaths in Burundi. The place of COVID-19 symptoms was sought among deaths that occurred from January to May 2020 (during the pandemic) vs. January to May 2019 (before the pandemic). First, death proportions were tested to seize differences between mortality rates for each month in 2020 vs. 2019. In the second time, we compared mean time-to-death between the two periods using the Kaplan-Meier survival curve. Finally, a logistic regression was fitted to assess the likelihood of dying from COVID-19 symptoms between the two periods. We found statistical evidence of a higher death rate in May 2020 as compared to May 2019. Moreover, death occurred faster in 2020 (mean = 6.7 days, s.d. = 8.9) than in 2019 (mean = 7.8 days, s.d. = 10.9). Unlike in 2019, being a male was significantly associated with a much lower likelihood of dying with one or more COVID-19 symptom(s) in 2020 (odds ratio 0.35, 95% confidence interval 0.14-0.87). This study yielded some evidence for a possible COVID-19-related hospital-based mortality trend for May 2020. However, considering the time-constraint of the study, further similar studies over a longer period of time need to be conducted to trace a clearer picture on COVID-19 implication on hospital-based deaths in Burundi.