Table 1.
Parameters description for the SIR-SI model.
Fig 1.
Temperature dependence of the parameters.
(a) Likelihood of a person becoming infectious λh, (b) Likelihood of a mosquito becoming infectious λm, (c) Bite rate β, (d) Rate of death of mosquitoes δm.
Fig 2.
Behavior of the human population over time in the SIR-SI model.
(a,c) Susceptible, (b,e) Infected and (c,f) Recovered. When ρ < 1 (a, b and c), and ρ > 1 (d,e and f).
Fig 3.
Schematic view of the SIR-SI metapopulation model.
(a) shows the compartmental dynamics of the SIR-SI model with cross-infections between vectors and humans while panel (b) shows a toy metapopulation of 3 patches connected through links whose weights are given by matrix ϒ.
Fig 4.
Schematic plot of the three simple metapopulations covered as case examples.
In panel (a), corresponding to case A, node 1 is a zero-incidence node, i.e., there are no mosquitoes on it, however in node 2, there are environmental conditions for vector breeding and the disease is present. Panel (b) corresponds to case B. In this case node 1 is defined as endemic and is connected to a non-endemic node. Note that in both nodes the disease is present, but in node 1 the vector breeding conditions are better, so there is a wider spread of the disease than in node 2. Finally in panel (c) we define case C for which there are three equidistant connected nodes. Node 1 has zero incidence and its population is the largest while nodes 2 and 3 have characteristics of endemic regions. However, node 2 has more inhabitants than node 3 and, therefore, the probability of infection in node 3 is larger than in the other two patches.
Fig 5.
Evolution of dengue cases in patches as the distance increases when connecting a) High and a zero incidence node. b) High and medium incidence node. c) High, medium and zero incidence node. The green dots show the dengue cases in the zero incidence node, the blue dots show the cases in the medium incidence node and the red dots show the cases in the high incidence node. The black dots are used for the total number of cases. Lines are added to guide the reader’s eye.
Fig 6.
Top row: Spatial distribution of the cases of Dengue reported in the department of Caldas in 2015 (Left panel) and that predicted by our model (Right panel). Bottom row: Cases predicted by the model as a function of the reported data in 2015 for each municipality in the department of Caldas. ρP indicates the Pearson correlation coefficient between these variables. The dashed grey line represent the perfect agreement scenario between model and real data. Source of the Base Layer: The base layer data used in this map was obtained from Health Observatory of Caldas through ArcGIS Open Data at Gobernación de Caldas website.
Fig 7.
Variation of dengue cases in the department Caldas according to mobility restrictions when: i) no foreigners are allowed to enter the municipality (blue), no inhabitants can leave the municipality (green), the municipality is totally isolated (orange).
Fig 8.
Variation of dengue cases in the department of Caldas when quarantine are implemented.
The green color indicates a reduction in the number of dengue cases, with darker tones representing more significant changes. Conversely, the red color symbolizes an increment in dengue cases after the quarantine are applied. Map Data Source: The data on this map are the result of simulations through ArcGIS Open Data at Gobernación de Caldas website.