ABSTRACT
Diverse modelling techniques in cholera epidemiology have been developed and used to (1) study its transmission dynamics, (2) predict and manage cholera outbreaks, and (3) assess the impact of various control and mitigation measures. In this study, we carry out a critical and systematic review of various approaches used for modelling the dynamics of cholera. Also, we discuss the strengths and weaknesses of each modelling approach. A systematic search of articles was conducted in Google Scholar, PubMed, Science Direct, and Taylor & Francis. Eligible studies were those concerned with the dynamics of cholera excluding studies focused on models for cholera transmission in animals, socio-economic factors, and genetic & molecular related studies. A total of 476 peer-reviewed articles met the inclusion criteria, with about 40% (32%) of the studies carried out in Asia (Africa). About 52%, 21%, and 9%, of the studies, were based on compartmental (e.g., SIRB), statistical (time series and regression), and spatial (spatiotemporal clustering) models, respectively, while the rest of the analysed studies used other modelling approaches such as network, machine learning and artificial intelligence, Bayesian, and agent-based approaches. Cholera modelling studies that incorporate vector/housefly transmission of the pathogen are scarce and a small portion of researchers (3.99%) considers the estimation of key epidemiological parameters. Vaccination only platform was utilized as a control measure in more than half (58%) of the studies. Research productivity in cholera epidemiological modelling studies have increased in recent years, but authors used diverse range of models. Future models should consider incorporating vector/housefly transmission of the pathogen and on the estimation of key epidemiological parameters for the transmission of cholera dynamics.
ABSTRACT
Various general and individual measures have been implemented to limit the spread of SARS-CoV-2 since its emergence in China. Several phenomenological and mechanistic models have been developed to inform and guide health policy. Many of these models ignore opinions about certain control measures, although various opinions and attitudes can influence individual actions. To account for the effects of prophylactic opinions on disease dynamics and to avoid identifiability problems, we expand the SIR-Opinion model of Tyson et al. (2020) to take into account the partial detection of infected individuals in order to provide robust modeling of COVID-19 as well as degrees of adherence to prophylactic treatments, taking into account a hybrid modeling technique using Richard's model and the logistic model. Applying the approach to COVID-19 data from West Africa demonstrates that the more people with a strong prophylactic opinion, the smaller the final COVID-19 pandemic size. The influence of individuals on each other and from the media significantly influences the susceptible population and, thus, the dynamics of the disease. Thus, when considering the opinion of susceptible individuals to the disease, the view of the population at baseline influences its dynamics. The results are expected to inform public policy in the context of emerging and re-emerging infectious diseases.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Pandemics/prevention & control , Africa, Western , Health Policy , Disease Susceptibility/epidemiologyABSTRACT
Uvaria chamae is a wild shrub species widely used as a source for traditional medicine, food and fuel in West Africa. The species is threatened by uncontrolled harvesting of its roots for pharmaceutical applications and by the extension of agricultural land. This study assessed the role of environmental variables for the current distribution and the potential impact of climate change on the future spatial distribution of U. chamae in Benin. We used data related to climate, soil, topography and land cover to model the distribution of the species. Occurrence data were combined with six least correlated bioclimatic variables derived from the WorldClim database, data on soil layers (texture and pH) and topography (slope) obtained from the FAO world database and land cover from the DIVA-GIS site. Random Forest (RF), Generalized Additive Models (GAM), Generalized Linear Models (GLM) and the Maximum Entropy (MaxEnt) algorithm were used to predict the current and future (2050-2070) distribution of the species. Two climate change scenarios (SSP245 and SSP585) were considered for the future predictions. The results showed that climate (i.e., water availability) and soil type are the key predictors of the distribution of the species. Based on future climate projections, RF, GLM and GAM models predict that the Guinean-Congolian and Sudano-Guinean zones of Benin will remain suitable for U. chamae, while it will decline in these zones according to the MaxEnt model. These results call for a timely management effort for the species in Benin through its introduction into agroforestry systems to ensure the continuity of its ecosystem services.
ABSTRACT
The use of biotechnological approaches to increase soil fertility and productivity allows to obtain sustainable agriculture with lesser use of chemical fertilizers. The present study aimed to determine whether the inoculation of Bacillus panthothenicus, Bacillus thuringiensis, Pseudomonas putida, Pseudomonas syringae, or Serratia marcescens combined with reduced doses of NPK (nitrogen, phosphorus, potassium) fertilizer can improve the growth and yield of maize on poor ferruginous soils under field conditions in central Benin. For this purpose, maize seeds of the EVDT 97 STR C1 variety were inoculated with 10 ml suspension of five plant growth-promoting rhizobacteria (PGPR) strains, and the plots were fertilized at seeding with the recommended doses (0, 25, 50, 100%) of 200 kg/ha of NPK and 100 kg of urea for corn cultivation. The study was conducted in a completely randomized design with 3 replicates. The results showed that except for P. syringae , which induced the highest fresh aerial biomass (94.51%) and dry aerial biomass (63.63%), all other parameters were positively improved with inoculation associated with reduced doses of NPK + urea. The best height, leaf area, fresh underground biomass, and grain yield were recorded in response to the application of P. syringae + 50% NPK + urea, with an increase of 26.82, 32.23, 107.57, and 30.64%, respectively, compared to those of the control. The inoculation of seeds with P. syringae + 50% NPK + urea can be considered to be an environmentally sustainable strategy for maize cultivation.
