Climatic features and associations with ENSO
The mean values of annual rainfall at Chahbahar meteorological station that emphasizes the severity of dryness over the study area is 120 mm for the period 1986–2016 (Table 1). Winter, spring, summer and autumn rainfalls constitute 53.5%, 8%, 4.5% and 34% of this rainfall, respectively. As indicated, the corresponding seasonal values of temperature or relative humidity were 22. °C, 29.3°C, 29.2°C and 25.1°C or 69%, 79%, 82% and 70 %, respectively. These statistics suggests that, in contrast to rainfall, relative humidity is generally greater for warm rather than cool months of the year.
Table 1
Mean values of seasonal rainfall, temperature and relative humidity in Chahbahar for the period 1986–2016. The corresponding values of these variables during the El Niño and La Niña are also presented.
| Rainfall mm | Temperature oC | Relative Humidity % |
Seasons | Mean | El Niño | La Niña | Mean | El Niño | La Niña | Mean | El Niño | La Niña |
Winter | 63.9 | 69.7 | 54.3 | 22.0 | 22.1 | 22.0 | 68.8 | 72.1 | 64.8 |
Spring | 9.6 | 1.1 | 19.5 | 29.3 | 29.1 | 29.3 | 79.4 | 78.4 | 78.0 |
Summer | 5.4 | 2.4 | 4.2 | 29.2 | 29.1 | 29.5 | 82.0 | 84.0 | 80.6 |
Autumn | 41.4 | 58.8 | 21.0 | 25.1 | 25.0 | 25.3 | 70.0 | 70.7 | 67.0 |
Annual | 120.3 | 132.0 | 99.0 | |
Mean | 26.4 | 26.3 | 26.5 | 75.1 | 76.3 | 72.6 |
* Difference in mean values is statistically significant between El Niño and La Niña (Mann-Whitney test) |
** Difference in the frequency is statistically significant El Niño and La Niña (Fisher Exact test) |
Autumn/winter rainfall is significantly/remarkably greater for the El Niño as compared with La Niña years. This relationship, however, is reversed for spring and summer, when the mean seasonal rainfall is negligible and deviates from 9.6 to 5.4 mm, respectively. Since rainfall time-series in Chahbahar are affected by frequent near zero and sporadic torrential rain, the presented ENSO-rainfall relationships could change over time for winter, spring and summer, when these relationships are not strongly significant. Compared to El Niño, air temperature tends to be warmer during La Niña episodes from 0.2°C to 0.4°C. Relative humidity, however, is consistently greater during El Niño episodes from about 0.4% in spring to 7% in winter.
The impact of ENSO on malaria potentially increases when the difference in the climatic features is significant between El Niño and La Niña, as shown in Table 1. This influence is, hence, more remarkable during autumn, winter, summer and spring, respectively.(Zubair et al., 2008) It is concluded that long-term records of malaria data which are less affected by the disease control programs are needed to establish the ENSO-malaria relationship.
Seasonal distribution of malaria
Table 2 illustrates the historical records of patient's statistics in twelve health centers during summertime, when total infection is in its highest status. Since the dataset of the other two stations contained some missing data, their statistics are not presented. Similar statistics were also obtained for the other seasons, but due to result briefing, these data are presented in appendix 1. As indicated, mean values of the number of patients for winter, spring, summer and autumn are correspondingly 267, 1501, 5159, and 2885. While the highest records are related to the hot and dry months, the lowest values are linked to winter when rainfall is remarkably higher than the other seasons. These suggest the significant impact of temperature rather than rainfall on the number of patients.
Table 2
; Historical records of the patient number during summertime; when the presented statistics are greater than the other seasons. The statistics of twelve health centers with lowest values of missed data are presented. The name of these centers is presented as the footnote of the Table.
Year | Station Number |
1* | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
Summer |
2002 | 4 | 25 | 25 | 6 | 28 | 37 | 3 | 13 | - | 185 | 92 | 8 |
2003 | 88 | 215 | 420 | 19 | 54 | 171 | 254 | 127 | - | 254 | 64 | 161 |
2004 | 15 | 14 | 44 | 8 | 6 | 44 | 40 | 17 | 2 | 104 | 61 | 20 |
2005 | 22 | 55 | 65 | 12 | 14 | 78 | 59 | 20 | 7 | 70 | 33 | 49 |
2006 | 2 | 15 | 53 | 3 | 5 | 23 | 9 | 3 | 3 | 36 | 17 | 2 |
2007 | 20 | 108 | 132 | 11 | 4 | 108 | 153 | 66 | 0 | 47 | 11 | 30 |
2008 | 14 | 49 | 312 | 5 | 1 | 83 | 50 | 19 | 6 | 26 | 4 | 21 |
2009 | 6 | 18 | 210 | 3 | 3 | 17 | 2 | 5 | 1 | 12 | 7 | 1 |
2010 | 1 | 26 | 105 | 2 | 2 | 9 | 1 | 7 | 1 | 34 | 15 | 7 |
2011 | 3 | 8 | 68 | 2 | 0 | 5 | 6 | 1 | 0 | - | - | 8 |
Average | 17 | 53 | 143 | 7 | 12 | 58 | 58 | 28 | 3 | 85 | 34 | 31 |
1.Arabzehi, 2.Bahookalat, 3.Darges, 4.Kambal Soleyman, 5.Nagoor, 6.Nobandian, 7.Pir Sohrab, 8. Plan, 9.Sangan, 10.Shahri1, 11.Shahri2, 12.Talang |
Infection Types
P. falciparum, P. vivax and the other types of malaria accounted for 22%, 75%, and 3% of infections, respectively. Figure 3 illustrates the spatial distribution of autumnal P. falciparum-infection when the frequency of this type of infection is greater than the other seasons. Similar illustrations related to other seasons are presented in Appendix 2. In spite of the fact that the infection statistics are different from season to season, the spatial patterns of the epidemic are in general agreement, suggesting that the frequency of seasonal infection is firstly the highest over the northeastern and secondly over the southwestern parts of the county (Fig. 3 and Appendix 2). According to Fig. 4, after autumn, the infection statistics are in the second to fourth ranks during summer, spring and winter, respectively.
Figure 5 is similar to Fig. 3 except for P. vivax and for summer; when this infection is more acute than the other seasons. Comparing Figs. 3 and 5 suggests that, although seasons and infection types are different, the geographical patterns of the infections are almost identical for both Figs. In spite of this coherence, patient number is more than three times for P. vivax in Fig. 5 as compared with P. falciparum in Fig. 3. After summer, the risk of P. vivax is, respectively, more severe during autumn, spring and winter (Fig. 6). The highest frequencies of P. falciparum and P. vivax are related to the year 2003 one of the driest year during the study period (Figs. 4 and 6). Our examination, however, did not exhibit any exceptional condition in climatic indices for this year. It is noteworthy to mention that the unusually high incidence of malaria is not necessarily driven by unusual weather patterns, and rather, is mediated by other factors such as human behavior, the mosquitos movement patterns, migration and agricultural practices (Wardrop et al., 2013).
ENSO phase and malaria
Table 3 illustrates the autumnal values of the number of patients for the warm and cold phases of the ENSO at Dagres health center as an example. The data are presented for two choices including the options that each of the ENSO phases contains 3 or 4 years. For instance, if three years with lowest or highest values of SOI are adopted as the El Niño or La Niña episodes, the mean values of the number of patients and their associated NMIEl/NMILa are 78.6, 101 and 0.78, respectively. For four years option, the statistics of the years 2004 or 2007 are also included as the El Niño or La Niña year, respectively.
Table 3
The magnitudes of autumnal SOI and their corresponding patient numbers and the ratio of NMIEl / NMILa for the El Niño and La Niña episodes. Patient numbers are related to Dagres health center.
Year | SOI | Patients' number | ENSO Status | Mean infection | NMIEl/NMILa |
2009 | -9.2 | 66 | El Niño | 78.6 | |
2002 | -8.0 | 42 | El Niño | |
2006 | -7.1 | 128 | El Niño | 78.6/101 = 0.78 |
2004 | -6.80 | 56 | El Niño If 4 years considered | 73 | 73/96 = 0.76 If 4 years considered |
2007 | 9.7* | 80 | La Niña if 4 years considered | 101 | |
2008 | 14.6 | 191 | La Niña | |
2011 | 14.7 | 44 | La Niña | |
2010 | 20.6 | 68 | La Niña If four | 96 | |
* The statistics related to the La Nina years are shown by bold and italic. |
Figure 7 depicts the spatial distribution of the NMIEl/NMILa for autumn, when the difference between NMIEl and NMILa is significant for most of the health centers. Similar Figures were also obtained for the other seasons, but they are presented in appendix 3. (Gagnon et al., 2002) We found a statistically significant relationship between El Niño and malaria epidemics in Colombia, Guyana, Peru, and Venezuela (Hanafi-Bojd et al., 2010).
As indicated in Fig. 7, in contrast to the northern and particularly northeastern parts of the county, for the urban areas in the southwestern corner of the study area, the ratio of NMIEl/NMILa is greater than unity and deviates from 1.5 to 2.36. This suggests about 50–136% increase in the infection statistics of urban areas during El Niño as compared with the corresponding statistics during La Niña events. According to this Fig, the effects of the ENSO phases on the number of patients are reversed between eastern and western sides of the county.
Impacts of the malaria elimination policies
Since ENSO is a global climate phenomenon, it is difficult to accept that the prevalence of the El Niño or La Niña induces an opposite effect on the malaria statistics of the study area, as presented in the previous section. Our close examination revealed that the implementation of malaria eradication policies by Iranian government is probably the main cause of the observed contradiction. While the number of infected people in urban regions has been consistently reduced from 2002 to 2011, such decline in the number of patients is not found for rural regions
Table 4 delineates the ratio of the number of patients during five years period of 2002–2006 to the corresponding statistics during 2007–2011. As indicated, except for one case (summer in Bahookalat), the ratio is consistently greater than unity. This indicates a considerable reduction in the infection frequency during the 2007–2011, as compared to its preceding five years. The reduction rate is, however, different between health centers and from one season to another. The biggest or smallest ratio values are generally associated to the urban (i.e. Shahri 2 and Shahri 3) or rural areas (i.e. Dagres and Bahookalat), respectively. Statistics in Table 4 prove that the malaria elimination policies are more effective in urban areas as compared to the rural parts of the county. The given results by(Fekri et al., 2014) and (Hanafi-Bojd et al., 2010) generally support these differences.
Table 4
The ratio of the infected people during the period 2002–2006 to the next five years period of 2007 to 2011. The number 1 to 12 refer to the station name as indicated in the footnote of this Table.
Season | Station Number |
1* | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
N(2002−2006)/N(2006−2011) |
Spring | - | 1.19 | 1.45 | - | 3.70 | 1.75 | 2.00 | 1.90 | - | 4.20 | 2.10 | 2.30 |
Summer | - | 1.40 | 0.80 | - | 3.80 | 1.85 | 1.75 | 1.6 | - | 4.10 | 2.40 | 3.15 |
Autumn | - | 1.10 | 1.05 | - | 2.80 | 2.50 | - | 2.05 | - | 5.05 | 2.45 | - |
Winter | - | 1.05 | 1.50 | - | 4.00 | 1.55 | 2.95 | 1.20 | - | 2.80 | 1.25 | - |
*The station name are as follow: |
1.Arabzehi, 2.Bahookalat, 3.Darges, 4.Kambal-Soleyman, 5.Nagoor, 6.Nobandian, 7.PirSohrab, 8. Plan, 9.Sangan, 10.Shahri1, 11.Shahri2, 12.Talang |
According to the SOI data, in the 10 years of the study period the autumnal SOI was negative for 4 years and positive for the remaining 6 years. As indicated in Table 3, while the El Niño events had mostly occurred during the first half of the study period, the La Niña years are frequently associated to the second half of this period when the effect of technology for immunity improvement is more obvious. The considered El Niño or La Niña events, therefore, coincide with the periods with low or high effects of technology on the infection statistics, respectively. This means that if the effects of technology on the malaria statistics were removed, El Niño or La Niña events would be generally associated to the decrease or increase in the malaria epidemic as indicated for Sangan, Arabzehi, Dagres and Bahookalat in Fig. 7. In other words, the ENSO-malaria relationships are more accurate if these relations are assessed for the rural rather than urban regions.
The ratio of Pw/pd
Table 5 depicts the mean values of autumnal rainfall for the assigned dry and wet periods at Dagres health center (Fig. 2), that is presented as an example. The corresponding numbers of patients (Pw and Pd) and the ratio of pw/pd are also presented. The three years with lowest or highest rainfall are 2005, 2007 and 2003 or 2011, 2006 and 2004, respectively. Mean values of rainfall for these dry and wet periods, their corresponding number of patients and the ratio of Pw/Pd are presented. These statistics suggest that the autumnal upsurge or decline in the number of patients in Dagres health center is statistically associated to the adopted dry or wet events, respectively. The results of the statistical tests remained stationary static when Pw and Pd were four years.
Table 5
The measure of rainfall, patient number, mean rainfall, mean infection and the ratio of Pw/Pd. All data are related to the Degres health center during autumn.
| Year | Rainfall mm | Patients' number | | Mean Rainfall mm/month | Mean patient | | The ratios of Pw/Pd |
Low Rainfall | 2005 | 0.74 | 84 | 151 | 2.6 (3 years | 71 | 120 | |
2007 | 2.72 | 53 | 80 | 4.8 (4 years) | 65 | 106 | |
2003 | 4.23 | 76 | 128 | | | | 51/71 = 0.72 |
| 2010 | 11.71 | 47 | 68 | | | | 52/6 5 = 0.78 |
| 2009 | 37.92 | 49 | 66 | | | | 76/120= |
High Rainfall | 2011 | 55.99 | 21 | 44 | | | | 74/104= |
2006 | 71.61 | 94 | 128 | 67.8 (3 years | 51 | 76 | |
2004 | 75.97 | 39 | 56 | 60.4(4 years) | 52 | 74 | |
Mean | | 32.61 | 58 | | | | | |
Table 6 is the seasonal values of Pw/Pd for all twelve stations. As indicated, ratios are consistently and significantly less than unity for autumn, winter and spring and greater than 1.0 for summer. This means that the occurrence of wet or dry episodes harmonize is in accordance with the reduction or increase in the number of patients during autumn, winter and spring, respectively. This relationship, however, is reversed for summer. Some direct or indirect malaria-rainfall associations were also reported by other investigators(Odongo-Aginya et al., 2005; Wardrop et al., 2013). Although precipitation provides essential habitat for larvae during the aquatic stages of some infectious diseases, drought can indirectly expand the vector’s range while other researchers have also reported some negative relationships between the amount of rainfall and malaria during persistent regional drought.
Table 6
Seasonal values of the ratio of Pw/pd for all stations and various stations.
Station name | Autumn | winter | Spring | Summer |
Darges | 0.72 | 0.75 | 0.33 | 0.41 |
Bahokalat | 0.62 | 0.95 | 0.43 | 1.97 |
sangan | 0.44 | | 0.19 | |
Nobandian | 0.47 | 0.91 | 0.34 | 1.85 |
Nagoor | 0.45 | 0.27 | 0.09 | 8.33 |
Kambal solyman | 0.24 | Almost no patient | Almost no patient | 1.73 |
Talang | 0.35 | 0.25 | 0.43 | 3.20 |
Pir sohrab | 0.08 | 0.50 | 0.11 | 1.62 |
Plan | 0.16 | 1.67 | 0.72 | 1.76 |
Arabzehi | 0.22 | 1.00 | 0.36 | 2.89 |
Shahri 2 | 0.33 | 0.42 | 0.20 | 2.02 |
Shari 3 | 0.40 | 0.30 | 0.43 | 1.08 |