 |
| Titel |
Integrating remote sensing and spatially explicit epidemiological modeling |
| VerfasserIn |
Flavio Finger, Allyn Knox, Enrico Bertuzzo, Lorenzo Mari, Didier Bompangue, Marino Gatto, Andrea Rinaldo |
| Konferenz |
EGU General Assembly 2015
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250108947
|
| Publikation (Nr.) |
 EGU/EGU2015-8790.pdf |
|
|
|
|
|
| Zusammenfassung |
| Spatially explicit epidemiological models are a crucial tool for the prediction of
epidemiological patterns in time and space as well as for the allocation of health care
resources. In addition they can provide valuable information about epidemiological
processes and allow for the identification of environmental drivers of the disease
spread.
Most epidemiological models rely on environmental data as inputs. They can either be
measured in the field by the means of conventional instruments or using remote
sensing techniques to measure suitable proxies of the variables of interest. The
later benefit from several advantages over conventional methods, including data
availability, which can be an issue especially in developing, and spatial as well as
temporal resolution of the data, which is particularly crucial for spatially explicit
models.
Here we present the case study of a spatially explicit, semi-mechanistic model applied to
recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The
model describes the cholera incidence in eight health zones on the shore of the lake.
Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation
and indices of global climate anomalies are used as environmental drivers. Human
mobility and its effect on the disease spread is also taken into account. Several model
configurations are tested on a data set of reported cases. The best models, accounting for
different environmental drivers, and selected using the Akaike information criterion,
are formally compared via cross validation. The best performing model accounts
for seasonality, El Niño Southern Oscillation, precipitation and human mobility. |
|
|
|
|
|
|