Biometeorological Distress and Influenza Epidemics on the East Coast of USA

INTRODUCTION

The study of the relationship between influenza and climate is a complex issue because of the number of aspects and factors that must be considered for the understanding of processes that link the disease and atmospheric patterns. Epidemic spreading responds mainly to the biological reasons of life spreading itself. In this sense, it is obvious that environmental factors play a key role on the process of triggering or neutralizing any epidemics but it is not an easy task to define the mechanisms through which a simple case can become or not a pandemic. The main aim of this research is to assess the applicability of the Meteorological Contrast Index (MCI) as a predictor of the influenza epidemics outbreaks on the East Coast of United States. This method has previously been tested in Spain for the epidemic period 2001-09 by Fdez-Arroyabe (2012). In this particular case, it was used as input for the estimation of the meteorological contrast a daily classification of circulation types adapted to the Iberian Peninsula by Rasilla et al. (2000).

The Spatial Synoptic Classification (SSC) has been used as input for the index calculation. The SSC was initially developed by Kalkstein et al. (1996) in the mid-1990s, and later on it was re-developed by Sheridan (2002). It is an automatic air mass-based classification method that it is daily applied for nearly 400 weather stations across the United States, Canada and some areas of Europe.

DATA SOURCES AND METHODOLOGY

Influenza data have been obtained from the reports generated by World Health Organization (WHO) and the National Respiratory and Enteric Virus Surveillance System Collaborating Laboratories (NREVS). Isolated influenza viruses cases by U.S. Health Regions are registered for each epidemic week. Epidemic periods from 2001-02 to 2006-07 have been studied in four U.S. East Health Regions. For each region, weekly epidemic information related to the total number of positive cases of influenza are registered considering types A(H1), A(H3), A without subtyping performed and B type. The ratio per one thousand between positive cases in relation to the total number of tested cases has been estimated for each health region. From a meteorological point of view, SSC weather types, Sheridan (2002) have been used. This information is available at http://sheridan.geog.kent.edu/ssc.html where a large list of results can be obtained for numerous locations. The four selected weather stations are located in Boston, MA (code BOS); New York, NY (code JFK); Philadelphia, PA (code PHI); and Atlanta, GA (code ATL). Daily weather types corresponding to seven categories (DP) Dry Polar; (DM) Dry Moderate; (DT) Dry Tropical; (MM) Moisture Moderate; (MP) Moist Polar; (MT) Moist Tropical and (T) Transitional have been used as inputs in the model estimations for the study period 2001-07.

The index is based on three parameters which express atmospheric contrast at a synoptic level. In order to calculate the parameters, it has to be previously defined an array of contrast in which transitions among defined weather types, are assigned a numeric value that represents the physiological impact of meteorological change in terms of biometeorological stress.

RESULTS AND CONCLUSIONS

This study confirms the idea of biometeorological distress as a trigger of the outbreaks in the influenza epidemics has produced interesting results. The impact varies among the different regions and epidemic years of the study period.

In general terms, the percentage of confirmed cases of influenza was higher in the epidemic periods 2003-04 and 2005-06, especially in the sanitary region of Philadelphia. Moreover, there are three periods, from 2001 to 2004, in which there is a clear delay of virus activity in the area of Boston with more than one peak in the disease evolution through time. Apart from these incidences, the evolution of influenza curves is relatively synchronic in the four study areas.

In the region with the highest percentage of influenza, Philadelphia zone, the biometeorological distress becomes a relevant factor in relation to the spreading of the disease. This is something that also happens in other regions but it cannot be defined a statistical model to express the influenza relation to biometeorological distress. Because maximum values of the meteorological contrast index vary from 35 in the region of Boston to 24 in then Atlanta zone, biometeorological impact must be considered a relative concept that makes sense in the specific context in which influenza epidemic takes place. Moreover, it is important to take into account microbiological issues such as the type of viruses that are registered during each epidemic season and the bias that the spatial variability of introduces in the analysis.

REFERENCES

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