scholarly journals Re-Imagining Resilient Food Systems in the Post-COVID-19 Era in Africa

2021 ◽  
Vol 13 (19) ◽  
pp. 10752
Author(s):  
Julian May ◽  
Melody Mentz-Coetzee
Keyword(s):  
Coping Strategies ◽  
Food Systems ◽  
Health Care Expenditure ◽  
Health Interventions ◽  
Risk And Uncertainty ◽  
Public Health Interventions ◽  
High Prevalence ◽  
Negative Impacts ◽  
Reproductive Activities ◽  
Initial Focus

The COVID-19 pandemic heightened awareness that serious illness and injury are common and important shocks that result in food insecurity, the loss of livelihoods, and unsustainable coping strategies. These have significant negative impacts on welfare, especially for the poorest, driving up health care expenditure, reducing capabilities for productive and reproductive activities, and decreasing capacity to manage climate and other changes. These negative impacts are especially pertinent for countries in Africa where the high prevalence of communicable diseases such as HIV/AIDS and malaria have resulted in repeated health shocks. Unusually, the prevalence of these illnesses results in their impact being similar to those of covariate shocks, increasing the risk of poverty for entire communities and reducing options for coping strategies. Livelihood disruptions arising from the COVID-19 pandemic may have similar consequences for African food systems. The pandemic is likely to exacerbate existing dynamics of risk and introduce new and unanticipated changes to food systems. Although the initial focus of governments has been on public health interventions, preserving and growing resilient food systems is critical if livelihoods are to be protected. This paper discusses the implications of these evolving forms of risk and uncertainty for sustainable African food systems, reflecting on lessons from other systemic shocks.

Prevalence of multiple sclerosis in Sardinia: A systematic cross-sectional multi-source survey

2019 ◽  
Vol 26 (3) ◽  
pp. 372-380 ◽  
Author(s):  
Silvana AM Urru ◽  
Antonello Antonelli ◽  
Giuseppe M Sechi ◽  
Keyword(s):  
Multiple Sclerosis ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Cross Sectional ◽  
Mediterranean Populations ◽  
Secondary Progressive ◽  
High Prevalence ◽  
Relapsing Remitting ◽  
Prevalence Estimates ◽  
Study Population

Background: Partial surveys in sub-regions of Sardinia have suggested a high prevalence of multiple sclerosis (MS) on the island, relative to other Mediterranean populations. We assessed the island-wide prevalence of MS and its detailed distribution in Sardinia. Methods: The study population consisted of 5677 MS patients, 1735 men and 3942 women, living in Sardinia. Neurologists retrospectively examined electronic and paper-based records of patients with a diagnosis of MS. The data were then linked to the administrative health information systems. Crude, age-, and sex-specific prevalence estimates of disease were calculated. Results: The overall age-adjusted MS prevalence was 330 per 100,000 (95% confidence interval (CI) 321–338) in individuals older than 15 years, 447 in women (95% CI 433–461), and 205 in men (95% CI 195–214). The prevalence was highest in the Ogliastra and Nuoro districts, respectively, 425 (95% CI 372–478) and 419 (95% CI 387–451), and lowest in the Olbia-Tempio district, 217 (95% CI 195–239). Most cases had relapsing–remitting MS (79.3%), 16.3% had secondary-progressive MS, and 4.4% had primary-progressive MS. Conclusion: These prevalence are among the highest reported so far worldwide. They provide estimates for comparative analyses in other populations and are essential for public health interventions.

scholarly journals An Accurate Mathematical Epidemiological Model (SEQIJRDS) to Recommend Public Health Interventions Related to COVID-19 in Sri Lanka.

2021 ◽  
Author(s):  
Patikiri Arachchige Don Shehan Nilmantha Wijesekara
Keyword(s):  
Public Health ◽  
Sri Lanka ◽  
Epidemiological Model ◽  
Health Interventions ◽  
Epidemiological Models ◽  
Public Health Interventions ◽  
The Public ◽  
Proposed Model ◽  
Practical Model ◽  
Negative Impacts

Abstract COVID-19 has been causing negative impacts on various sectors in Sri Lanka as a result of the public health interventions that government had to implement in order to reduce the spreading of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This research is carried out in a crucial time which the daily deaths are rapidly increasing which arise the requirement for an accurate and practical model to predict the mortality in order to take decisions regarding public health interventions. This paper presents a mathematical epidemiological model called SEQIJRDS to predict on COVID-19. The model has been validated for the COVID 19 pandemic in Sri Lanka. The results show that the model outstands many of the state-of-the-art SEIR epidemiological models such as Imperial, IHME once properly parameterized. At the end; this work recommends public health interventions at this crucial time to save people's lives based on the predictions of the proposed model. Specifically, 3 recommendations called minimal, sub-optimal and optimal recommendations are provided for public health interventions.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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