Lanfri S, Espinosa M, Lanfri MA, Periago VM, Abril M, et al. (2019) Interaction between Spatial and Temporal Scales for Entomological Field Data: Analysis of Aedes Aegypti Oviposition Series. J Infect Dis Epidemiol 5:087.


© 2019 Lanfri S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ORIGINAL ARTICLE | OPEN ACCESS DOI: 10.23937/2474-3658/1510087

Interaction between Spatial and Temporal Scales for Entomological Field Data: Analysis of Aedes Aegypti Oviposition Series

Sofia Lanfri1,2*, Manuel Espinosa1, Mario A Lanfri2, Victoria M Periago1,3, Marcelo Abril1 and Carlos M Scavuzzo2

1Fundación Mundo Sano, CABA, Argentina

2Instituto Gulich, CONAE, UNC, Cordoba, Argentina

3Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina



In Argentina, Aedes aegypti represents an important public health threat, since it is the vector responsible for the transmission of dengue, chikungunya, zika and yellow fever. Mundo Sano Foundation has been carrying out periodic surveys of immature vector stages in several cities of northern Argentina. The main tool to mitigate their spread is through vector control. The identification of vector "hot spots" is an important key to design preventive program tools. Geostatistical techniques such as spatial autocorrelation (SAC) and kriging interpolation can be used to predict vector abundance in unsampled areas using data obtained from monitored sites. The knowledge of the spatial autocorrelation of vector abundance is fundamental and it can also be used to design disease surveillance strategies: To determine the characteristics of chemical control; to select ovitrap placement (distance between samples); and to determine the optimum sample size, among others. It is important to analyze the effect of the variation of the scale in the observed phenomenon.


This paper analyzes a two years series of weekly oviposition data from 25 ovitraps distributed in the urban area of a small city (104 measurements were collected for each ovitrap). We aim to understand how the relationship between sites measurements varies considering its relative location in the city, for different temporal sampling frequency or temporal resolution (TR). Different similarity measures between curves and graphic representations of these relationships, are explored. Among these, an innovative use of polar graphs -a tool commonly used to detect changes in satellite images- is examined. We evaluate variograms and SAC for multitemporal data (oviposition curves) at each TR.


Similarity between curves does not show spatial continuity in relation to the spatial arrangement of ovitraps, may be due to the effect of processes that are only observable at the microhabitat scale or due to sociodemographic factors. As the temporal resolution is greater in a given area, a greater number of ovitraps are needed to capture the spatial heterogeneity of the abundance of the vector. At the maximum TR analyzed, the minimum distance of spatial correlations was set at 1000 m. This has implications on the quantity of ovitraps per area unit required in the field in order to obtain a good description of the population dynamics of Ae. aegypti at the peridomestic level.


The results would indicate that when varying the time scale of analysis, the spatial scale should be modified accordingly to adapt to the new data structure. The ability to predict ecological phenomena depends on the relationships between spatial and temporal scales. The approach and innovative statistical tools described in this study, based on empirical data from a field study, may be used by different Ae. aegypti monitoring and control programs in order to design and implement tailor-made interventions. It would allows to support not only the selection of field samples, and to obtain data interpolation parameters, but also to contribute to the development of vector abundance models.