COVID-19 Pandemic Impacted Food Security and Caused Psychosocial Stress in Selected States of Nigeria

The COVID-19 disease has infected many countries, causing generalized impacts on different income categories. We carried out a survey among households (n = 412) representing different income groups in Nigeria. We used validated food insecurity experience and socio-psychologic tools. Data obtained were analyzed using descriptive and inferential statistics. The earning capacities of the respondents ranged from 145 USD/month for low-income earners to 1945 USD/month for high-income earners. A total of 173 households (42%) ran out of food during the COVID-19 pandemic. All categories of households experienced increasing dependency on the general public and a perception of increasing insecurity, with the high-income earners experiencing the greatest shift. In addition, increasing levels of anger and irritation were experienced among all categories. Of the socio-demographic variables, only gender, educational level of the household head, work hours per day, and family income based on society class were associated (p < 0.05) with food security and hunger due to the COVID-19 pandemic. Although psychological stress was observed to be greater in the low-income earning group, household heads with medium and high family income were more likely to have satisfactory experiences regarding food security and hunger. It is recommended that socio-economic groups should be mapped and support systems should target each group to provide the needed support in terms of health, social, economic, and mental wellness.

COVID-19 Pandemic Impacted Food Security and Caused Psychosocial Stress in Selected States of Nigeria.

The COVID-19 disease has infected many countries, causing generalized impacts on different income categories. We carried out a survey among households (n = 412) representing different income groups in Nigeria. We used validated food insecurity experience and socio-psychologic tools. Data obtained were analyzed using descriptive and inferential statistics. The earning capacities of the respondents ranged from 145 USD/month for low-income earners to 1945 USD/month for high-income earners. A total of 173 households (42%) ran out of food during the COVID-19 pandemic. All categories of households experienced increasing dependency on the general public and a perception of increasing insecurity, with the high-income earners experiencing the greatest shift. In addition, increasing levels of anger and irritation were experienced among all categories. Of the socio-demographic variables, only gender, educational level of the household head, work hours per day, and family income based on society class were associated (p < 0.05) with food security and hunger due to the COVID-19 pandemic. Although psychological stress was observed to be greater in the low-income earning group, household heads with medium and high family income were more likely to have satisfactory experiences regarding food security and hunger. It is recommended that socio-economic groups should be mapped and support systems should target each group to provide the needed support in terms of health, social, economic, and mental wellness.

Public Health Surveillance for Adverse Events Following COVID-19 Vaccination in Africa.

Local, national, and international health agencies have advocated multi-pronged public health strategies to limit infections and prevent deaths. The availability of safe and effective vaccines is critical in the control of a pandemic. Several adverse events have been reported globally following reception of different vaccines, with limited or no data from Africa. This cross-sectional epidemiological study investigated adverse events following COVID-19 vaccination in Africans from April-June, 2021 using a structured online questionnaire. Out of 1200 participants recruited, a total of 80.8% (n = 969) respondents from 35 countries, including 22 African countries and 13 countries where Africans live in the diaspora, reported adverse events. Over half of the vaccinees were male (53.0%) and frontline healthcare workers (55.7%), respectively. A total of 15.6% (n = 151) reported previous exposure to SARS-CoV-2, while about one-fourth, 24.8% (n = 240), reported different underlying health conditions prior to vaccination. Fatal cases were 5.1% (n = 49), while other significant heterogenous events were reported in three categories: very common, common, and uncommon, with the latter including enlarged lymph nodes 2.4% (n = 23), menstrual disorder 0.5% (n = 5), and increased libido 0.2% (n = 2). The study provided useful data for concerned authorities and institutions to prepare plans that will address issues related to COVID-19 vaccines.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections.

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Will Africans take COVID-19 vaccination?

The economic and humanistic impact of COVID-19 pandemic is enormous globally. No definitive treatment exists, hence accelerated development and approval of COVID-19 vaccines, offers a unique opportunity for COVID-19 prevention and control. Vaccine hesitancy may limit the success of vaccine distribution in Africa, therefore we assessed the potentials for coronavirus vaccine hesitancy and its determinants among Africans. An online cross-sectional African-wide survey was administered in Arabic, English, and French languages. Questions on demographics, self-reported health status, vaccine literacy, knowledge and perception on vaccines, past experience, behavior, infection risk, willingness to receive and affordability of the SARS-COV-2 vaccine were asked. Data were subjected to descriptive and inferential statistics. A total of 5,416 individuals completed the survey. Approximately, 94% were residents of 34 African countries while the other Africans live in the Diaspora. Only 63% of all participants surveyed were willing to receive the COVID-19 vaccination as soon as possible and 79% were worried about its side effects. Thirty-nine percent expressed concerns of vaccine-associated infection. The odds of vaccine hesitancy was 0.28 (95% CI: 0.22, 0.30) among those who believed their risk of infection was very high, compared to those who believed otherwise. The odds of vaccine hesitancy was one-fifth (OR = 0.21, 95% CI: 0.16, 0.28) among those who believed their risk of falling sick was very high, compared to those who believed their risk of falling very sick was very low. The OR of vaccine hesitancy was 2.72 (95% CI: 2.24, 3.31) among those who have previously refused a vaccine for themselves or their child compared to counterparts with no self-reported history of vaccine hesitancy. Participants want the vaccines to be mandatory (40%), provided free of charge (78%) and distributed in homes and offices (44%). COVID-19 vaccine hesitancy is substantial among Africans based on perceived risk of coronavirus infection and past experiences.

The one health landscape in Sub-Saharan African countries.

OBJECTIVES: One Health is transiting from multidisciplinary to transdisciplinary concepts and its viewpoints should move from 'proxy for zoonoses', to include other topics (climate change, nutrition and food safety, policy and planning, welfare and well-being, antimicrobial resistance (AMR), vector-borne diseases, toxicosis and pesticides issues) and thematic fields (social sciences, geography and economics). This work was conducted to map the One Health landscape in Africa. METHODS: An assessment of existing One Health initiatives in Sub-Saharan African (SSA) countries was conducted among selected stakeholders using a multi-method approach. Strengths, weaknesses, opportunities and threats to One Health initiatives were identified, and their influence, interest and impacts were semi-quantitatively evaluated using literature reviews, questionnaire survey and statistical analysis. RESULTS: One Health Networks and identified initiatives were spatiotemporally spread across SSA and identified stakeholders were classified into four quadrants. It was observed that imbalance in stakeholders' representations led to hesitation in buying-in into One Health approach by stakeholders who are outside the main networks like stakeholders from the policy, budgeting, geography and sometimes, the environment sectors. CONCLUSION: Inclusion of theory of change, monitoring and evaluation frameworks, and tools for standardized evaluation of One Health policies are needed for a sustained future of One Health and future engagements should be outputs- and outcomes-driven and not activity-driven. National roadmaps for One Health implementation and institutionalization are necessary, and proofs of concepts in One Health should be validated and scaled-up. Dependence on external funding is unsustainable and must be addressed in the medium to long-term. Necessary policy and legal instruments to support One Health nationally and sub-nationally should be implemented taking cognizance of contemporary issues like urbanization, endemic poverty and other emerging issues. The utilization of current technologies and One Health approach in addressing the ongoing pandemic of COVID-19 and other emerging diseases are desirable. Finally, One Health implementation should be anticipatory and preemptive, and not reactive in containing disease outbreaks, especially those from the animal sources or the environment before the risk of spillover to human.

Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases.

Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay. Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = -0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception -a country where isolation was not pursued-, all countries showed a negative correlation between TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.

Statistical methods for comparing test positivity rates between countries: Which method should be used and why?

The test positivity (TP) rate has emerged as an important metric for gauging the illness burden due to COVID-19. Given the importance of COVID-19 TP rates for understanding COVID-related morbidity, researchers and clinicians have become increasingly interested in comparing TP rates across countries. The statistical methods for performing such comparisons fall into two general categories: frequentist tests and Bayesian methods. Using data from Our World in Data (ourworldindata.org), we performed comparisons for two prototypical yet disparate pairs of countries: Bolivia versus the United States (large vs. small-to-moderate TP rates), and South Korea vs. Uruguay (two very small TP rates of similar magnitude). Three different statistical procedures were used: two frequentist tests (an asymptotic z-test and the 'N-1' chi-square test), and a Bayesian method for comparing two proportions (TP rates are proportions). Results indicated that for the case of large vs. small-to-moderate TP rates (Bolivia versus the United States), the frequentist and Bayesian approaches both indicated that the two rates were substantially different. When the TP rates were very small and of similar magnitude (values of 0.009 and 0.007 for South Korea and Uruguay, respectively), the frequentist tests indicated a highly significant contrast, despite the apparent trivial amount by which the two rates differ. The Bayesian method, in comparison, suggested that the TP rates were practically equivalent-a finding that seems more consistent with the observed data. When TP rates are highly similar in magnitude, frequentist tests can lead to erroneous interpretations. A Bayesian approach, on the other hand, can help ensure more accurate inferences and thereby avoid potential decision errors that could lead to costly public health and policy-related consequences.

The potential of putative zinc-binding motifs of haemagglutinin (HA) protein for categorization and prediction of pathogenicity of H5 subtypes of avian influenza virus.

In the present study, we used the potential of bioinformatics and computational analysis to predict the existence and biological relevance of zinc finger (ZF) motifs in heamagglutinin (HA) protein of Avian Influenza (AI) virus. Sequence data of Avian Influenza (AI) viruses were retrieved from accessible databases (GenBank, GISAID, IRD) and analyzed for the existence, as well as functional prediction of the putative zinc finger or ''zinc-binding'' motif(s) of HA protein. It is hypothesized that the ZF motif(s) in HA of AI virus can be used as a ''novel'' biomarker for categorization of the virus and/or its virulence. As a model for analysis, we used the H5 subtypes of highly pathogenic, non-pathogenic and low pathogenic avian influenza (HPAI, NPAI and LPAI) viruses of H5N1 and H5N2 of avian and human origins. Interestingly, our method of characterization using the zinc-finger agrees with the existing classification in distinguishing between highly pathogenic and non-pathogenic or low pathogenic subtypes. The new method also clearly distinguished between low and non-pathogenic strains of H5N2 and H5N1 which are indistinguishable by the existing method that utilizes the sequence of the polybasic amino acids of the proteolytic cleavage site for pathogenicity. It is hypothesized that zinc through the activities of zinc-binding proteins modulates the virulence property of the viral subtypes. Our observation further revealed that only the HA protein among the eight encoded proteins of influenza viruses contain high numbers of Cys-His residues. It is expected that the information gathered from the analysis of the data will be useful to generate more research hypotheses/designs that will give further insight towards the identification and control of avian influenza virus through the molecular manipulation of zinc finger motifs present in viral HA protein.

Early network properties of the COVID-19 pandemic - The Chinese scenario.

OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.