Spatial dynamics of malaria transmission

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework—human population strata, patches, and aquatic habitats—interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the “vectorial capacity matrix,” a human transmitting capacity distribution matrix, and threshold conditions. An package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems

Long-term storage of sweetpotato by small-scale farmers through improved post harvest technologies

Sweetpotato (SP) small-scale farmers of Luweero and Mpigi districts were introduced to improved long-term storage methods (pit and clamp) as a way of improving their livelihood

Long-term storage of sweetpotato by small-scale farmers through improved post harvest technologies

No Abstract

Blood feeding and human biting rates.

The daily human biting rates (HBR) for the resident population strata are defined as the expected number of bites by vectors, per person, per day. To compute the HBR, we count up exposure over all the patches where residents spend time. We also consider the presence of visitors and other blood hosts (yellow input), which increases the total available hosts.</p

Denominators and mixing.

A schematic diagram relating various concepts of population density under a model of human mobility, resulting in a biting distribution matrix, β. Here, and and in Figs 3–6, rounded rectangles denote endogenous state variables, sharp rectangles denote endogenous dynamical quantities, and parallelograms represent exogenous data or factors. Purple indicates the element is related to human populations, green for mosquitoes, and red for biting and transmission. Population strata (H) describe how persons are allocated across demographic characteristics. The matrix distributes these strata across space (patch), according to place of residency. By combining information on how people spend their time across space (Θ(t)) and mosquito activity (ξ(t)) a time at risk (TaR) matrix Ψ is generated describing how person-time at risk is distributed across space. Because blood feeding can be modified by human and mosquito factors (e.g., net use and biting preferences), search weights (wf(t)) may further weight person-time at risk. The final result is a biting distribution matrix β, which is the fraction of each bite in each patch that would arise on an individual in each stratum, so diag(H) ⋅ β = 1.</p

Fig 9 -

(Left): bulk transmission metric describing transmission from the most densely populated area in Malabo, the capitol city, seen as the bright cell in the Northern tip of the island, to all other populated areas. (Right): bulk transmission from the most highly populated area in the south of the island (Luba), seen as the bright cell in the small harbor on the Western coast of the island. The base layer was created by to support malaria control operations [62] and shared under a CC BY 4.0 license. It is [available online] at https://figshare.com/articles/online_resource/Shape_Files_for_Bioko_Island_Equatorial_Guinea/22287580.</p

Fig 6 -

To model malaria importation, we define a travel FoI for each stratum, δ(t), and two set of terms to model the role of visitors in mosquito blood feeding and parasite transmission: the available visitor population Wδ and the NI for the visitor population, by patch xδ. To model blood feeding and transmission, we compute a patch-specific resident fraction for blood feeding, υ, the fraction of all biting that occurs on a resident of the spatial domain. From this, we can compute the visitor reservoir fraction, γ, the travel fraction for incidence, and other measures of malaria importation.</p