This study integrated accessibility and location-allocation models in geographic information systems as a proposed strategy to improve the spatial planning of public health services. To estimate the spatial accessibility, we modified the two-step floating catchment area (2SFCA) model with a different impedance function, a Gaussian weight for competition among service sites, a friction coefficient, distances along a street network based on the Dijkstra’s algorithm and by performing a vectorial analysis. To check the accuracy of the strategy, we used the data from the public sterilization program for the dogs and cats of Bogot´a, Colombia. Since the proposed strategy is independent of the service, it could also be applied to any other public intervention when the capacity of the service is known. The results of the accessibility model were consistent with the sterilization program data, revealing that the western, central and northern zones are the most isolated areas under the sterilization program. Spatial accessibility improvement was sought by relocating the sterilization sites using the maximum coverage with finite demand and the p-median models. The relocation proposed by the maximum coverage model more effectively maximized the spatial accessibility to the sterilization service given the non-uniform distribution of the populations of dogs and cats throughout the city. The implementation of the proposed strategy would provide direct benefits by improving the effectiveness of different public health interventions and the use of financial and human resources.

Brazilian Spotted Fever (BSF), caused by the bacterium Rickettsia rickettsii, is the tick-borne disease that generates the largest number of human deaths in the world. In Brazil, the current increase of BSF human cases has been associated with the presence and expansion of capybaras Hydrochoerus hydrochaeris, which act as primary hosts for the tick Amblyomma sculptum, vector of the R. rickettsii in this area. We proposed a semi-discrete-time stochastic model to evaluate the role of capybaras in the transmission dynamics of R. rickettsii. Through a sensitivity analysis, we identified the parameters with significant influence on the R. rickettsii establishment. Afterward, we implemented the Gillespie’s algorithm to simulate the impact of potential public health interventions to prevent BSF human cases. The introduction of a single infected capybara with at least one infected attached tick is enough to trigger the disease in a non-endemic area. We found that to avoid the formation of new BSF-endemic areas, it is crucial to impede the emigration of capybaras from endemic areas by reducing their birth rate by more than 58%. Model results were corroborated by ex-situ data generated from field studies, and this supports our proposal to prevent BSF human cases by implementing control strategies focused on capybaras. The proposed stochastic model illustrates how strategies for the control and prevention of vector-borne infectious diseases can be focused on amplifier hosts management practices. This work provides a basis for future prevention strategies for other neglected vector-borne diseases. A stochastic model for the spread of R. rickettsii among the Amblyomma sculptum tick vector and the Hydrochoerus hydrochaeris, amplifier host, was formulated. We found that the introduction of a single infected capybara, with at least one infected attached tick, is enough to trigger the disease in a non-endemic area. To avoid disease propagation, it is crucial to impede the emigration of capybaras from endemic areas by keeping the population in a stable state. Otherwise, to eliminate the disease from endemic areas, a reduction by more than 58% in the capybara birth rate is necessary. These results allow improving the planning of public actions to prevent Brazilian Spotted fever based on the control of capybara population and provided a basis for planning prevention programs for other neglected vector-borne diseases.

Covering: 2008 to 2018 Over the last decade more than two hundred single natural products were confirmed as natural allosteric modulators (alloNPs) of proteins. The compounds are presented and discussed with the support of a chemical space, constructed using a principal component analysis (PCA) of molecular descriptors from chemical compounds of distinct databases. This analysis showed that alloNPs are dispersed throughout the majority of the chemical space defined by natural products in general. Moreover, a cluster of alloNPs was shown to occupy a region almost devoid of allosteric modulators retrieved from a dataset composed mainly of synthetic compounds, further highlighting the importance to explore the entire natural chemical space for probing allosteric mechanisms. The protein targets which alloNPs bind to comprised 81 different proteins, which were classified into 5 major groups, with enzymes, in particular hydrolases, being the main representative group. The review also brings a critical interpretation on the mechanisms by which alloNPs display their molecular action on proteins. In the latter analysis, alloNPs were classified according to their final effect on the target protein, resulting in 3 major categories: (i) local alteration of the orthosteric site; (ii) global alteration in protein dynamics that change function; and (iii) oligomer stabilisation or protein complex destabilisation via protein–protein interaction in sites distant from the orthosteric site. G-protein coupled receptors (GPCRs), which use a combination of the three types of allosteric regulation found, were also probed by natural products. In summary, the natural allosteric modulators reviewed herein emphasise their importance for exploring alternative chemotherapeutic strategies, potentially pushing the boundaries of the druggable space of pharmacologically relevant drug targets.