No Arabic abstract
African Swine Fever (ASF) is viral infection which causes acute disease in domestic pigs and wild boar. Although the virus does not cause disease in humans, the impact it has on the economy, especially through trade and farming, is substantial. Recent rapid propagation of the (ASF) from East to West of Europe encouraged us to prepare risk assessment for Poland. The early growth estimation can be easily done by matching incidence trajectory to the exponential function, resulting in the approximation of the force of infection. With these calculations the basic reproduction rate of the epidemic, the effective outbreaks detection and elimination times could be estimated. In regression mode, 380 Polish counties (poviats) have been analysed, where 18 (located in Northeast Poland) have been affected (until August 2017) for spatial propagation (risk assessment for future). Mathematical model has been applied by taking into account: swine amount significance, disease vectors (wild boards) significance. We use pseudogravitational models of short and longrange interactions referring to the socio-migratory behavior of wild boars and the pork production chain significance. Spatial modeling in a certain range of parameters proves the existence of a natural protective barrier within boarders of the Congress Poland. The spread of the disease to the Greater Poland should result in the accelerated outbreak of ASF production chain. In the preliminary setup, we perform regression analysis, network outbreak investigation, early epidemic growth estimation and simulate landscape-based propagation.
Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.
An epidemiological model is developed for the spread of COVID-19 in South Africa. A variant of the classical compartmental SEIR model, called the SEIQRDP model, is used. As South Africa is still in the early phases of the global COVID-19 pandemic with the confirmed infectious cases not having peaked, the SEIQRDP model is first parameterized on data for Germany, Italy, and South Korea - countries for which the number of infectious cases are well past their peaks. Good fits are achieved with reasonable predictions of where the number of COVID-19 confirmed cases, deaths, and recovered cases will end up and by when. South African data for the period from 23 March to 8 May 2020 is then used to obtain SEIQRDP model parameters. It is found that the model fits the initial disease progression well, but that the long-term predictive capability of the model is rather poor. The South African SEIQRDP model is subsequently recalculated with the basic reproduction number constrained to reported values. The resulting model fits the data well, and long-term predictions appear to be reasonable. The South African SEIQRDP model predicts that the peak in the number of confirmed infectious individuals will occur at the end of October 2020, and that the total number of deaths will range from about 10,000 to 90,000, with a nominal value of about 22,000. All of these predictions are heavily dependent on the disease control measures in place, and the adherence to these measures. These predictions are further shown to be particularly sensitive to parameters used to determine the basic reproduction number. The future aim is to use a feedback control approach together with the South African SEIQRDP model to determine the epidemiological impact of varying lockdown levels proposed by the South African Government.
The Mediterranean Sea is one of the most heavily invaded marine regions. This work focuses on the dynamics and potential policy options for ballast water-mediated nonindigenous species to the Mediterranean. Specifically, we (1) estimated port risks in years 2012, 2015, and 2018, (2) identified hub ports that connect many clusters, and (3) evaluated four regulatory scenarios. The risk results show that Gibraltar, Suez, and Istanbul remained high-risk ports from 2012-2018, and they served as hub ports that connected several spread clusters. With policy scenario analysis, we found that regulating the high-risk hub ports can disproportionately reduce the overall risk to the Mediterranean: the average risk to all ports was reduced by 5-10% by regulating one high-risk hub port, while the average risk to all ports was only reduced by 0.2% by regulating one average-risk Mediterranean port. We also found that only regulating high-risk ports cannot reduce their risks effectively.
The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.
We develop an agent-based model on a network meant to capture features unique to COVID-19 spread through a small residential college. We find that a safe reopening requires strong policy from administrators combined with cautious behavior from students. Strong policy includes weekly screening tests with quick turnaround and halving the campus population. Cautious behavior from students means wearing facemasks, socializing less, and showing up for COVID-19 testing. We also find that comprehensive testing and facemasks are the most effective single interventions, building closures can lead to infection spikes in other areas depending on student behavior, and faster return of test results significantly reduces total infections.