An Exploratory Analysis of Malaria and Climatic Factors in India

Sachin Yadav Bodke

1 a

and Usha Ananthakumar

2 b

Industrial Engineering and Operations Research, IIT Bombay, India

Shailesh J. Mehta School of Management, IIT Bombay, India

Keywords:

India, Malaria Transmission, Seasonality, Rainfall, Temperature.

Abstract:

Malaria remains a signiﬁcant health challenge in India, prompting a thorough analysis of cases in recent years

from 2020 to 2022. This study focuses on understanding the spread of malaria over time and across different

states, speciﬁcally emphasizing the impact of climatic factors such as rainfall and temperature. India’s di-

verse climatic conditions, ranging from hot summers to cold winters, contribute to the complexity of malaria

dynamics. Variations in malaria prevalence were observed with changes in rainfall and temperature, particu-

larly during the months of July to October. Our ﬁndings reveal a notable increase in malaria cases during a

period characterized by signiﬁcant rainfall and temperature. The study identiﬁes a signiﬁcant prevalence of

malaria cases in India’s West, East, and North East regions with peak transmission occurring in the rainy sea-

son months. Considering the intricate interplay between climatic factors and disease transmission, this study

contributes valuable insights for tailored malaria control strategies during heightened transmission periods.

1 INTRODUCTION

Malaria, a life-threatening vector-borne disease trans-

mitted by infected Anopheles mosquitoes, remains a

signiﬁcant cause of morbidity and mortality world-

wide, particularly in tropical and subtropical regions.

This study focuses on the spatio-temporal analysis of

malaria in India, a country characterized by diverse

climates and a large population.

In 2021, nearly half of the global population was

at risk of malaria, with an estimated 247 million cases

reported worldwide, slightly higher than the 245 mil-

lion cases in 2020. The majority of cases in 2021 (95

percent) were concentrated in the WHO African re-

gion, while the WHO South-East Asia Region and the

WHO Eastern Mediterranean Region accounted for

2 percent and 3 percent, respectively. The COVID-

19 pandemic led to disruptions in essential malaria

services including reporting of Malaria deaths, thus

witnessing a decline from 625000 in 2020 to 619000

in 2021. The percentage of total malaria deaths in

children under 5 years decreased from 87% in 2000

to 76% in 2015, with no signiﬁcant change since

then (WHO, 2022). The WHO South-East Asia Re-

gion, with nine malaria-endemic countries in 2021,

accounted for 5.4 million cases and 9,000 deaths, con-

https://orcid.org/0009-0000-8947-455X

https://orcid.org/0000-0003-1983-2168

tributing 2% to the global burden of malaria cases.

Over the past two decades, the region has witnessed a

76% reduction in malaria cases, from 22.8 million in

2000 to 5.4 million in 2021, and an 82% decline in in-

cidence, from 17.9 to 3.2 per 1000 population at risk.

India, in particular, represented 79% of all malaria

cases in the region in 2021, with about 40% attributed

to the P. vivax strain. Certain Indian states, including

Jharkhand, West Bengal, Uttar Pradesh, Chhattisgarh,

Odisha, Gujarat, and Madhya Pradesh, consistently

reported high malaria cases (WHO, 2022).

Approximately 95% of India’s population resides

in malaria-endemic areas, with the majority of cases

(80%) originating from tribal, hilly, and inaccessible

regions where about 20% of the population lives. No-

tably, from 2020 to 2022, there were 506,764 reported

malaria cases and 262 reported deaths due to malaria

in India (NVBDCP, 2022). The continued prevalence

of malaria in speciﬁc geographic and demographic ar-

eas underscores the importance of targeted interven-

tions and sustained efforts to control and eliminate the

disease.

1.1 GIS in Malaria Research

Geographic Information System (GIS) plays a piv-

otal role in malaria research and control, offering

a versatile tool for various applications. GIS has

Bodke, S. and Ananthakumar, U.

An Exploratory Analysis of Malaria and Climatic Factors in India.

DOI: 10.5220/0012817400003756

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pages 159-168

ISBN: 978-989-758-707-8; ISSN: 2184-285X

159

proven effective in the creation of base maps, spot

maps, and different maps. It excels in mapping vec-

tor breeding habitats, delineating malaria risk zones,

and analyzing regions with high disease prevalence.

By utilizing GIS, researchers can generate insightful

maps that aid in understanding past and present dis-

ease trends. By mapping malaria incidence and risk,

decision-makers can identify priority areas for inter-

vention, optimizing resource allocation and response

strategies. This targeted approach enhances the ef-

ﬁciency of disease control efforts. Moreover, GIS-

based mapping has proven invaluable in monitoring

and evaluating malaria control activities on a broader

scale. This technology facilitates the visual represen-

tation of control measures’ impact over time, enabling

authorities to assess the effectiveness of implemented

strategies. This, in turn, contributes to the reﬁnement

and improvement of ongoing malaria control initia-

tives.

Noteworthy examples of GIS application in

malaria research and control extend globally. Several

countries, including various African nations, Brazil,

Sri Lanka, Thailand, the Republic of Korea, Indone-

sia, and Malaysia, have harnessed GIS to enhance

their understanding of malaria dynamics and optimize

control measures (Saxena et al., 2009). In the context

of India, GIS-based studies have been conducted in

speciﬁc districts. Notably, in 2007, GIS was ﬁrst in-

corporated into the national control program for tribal

malaria in Madhya Pradesh (Srivastava et al., 2009).

This integration marked a signiﬁcant stride in lever-

aging GIS for targeted interventions and data-driven

decision-making in the ﬁght against malaria.

1.2 Climatic Factors and Malaria

Climatic conditions signiﬁcantly inﬂuence the

mosquito’s life cycle and the malaria parasite’s

development. Numerous studies have established a

correlation between malaria incidence and various

climatic factors, including rainfall, relative humidity,

and temperature. Investigating the relationship be-

tween malaria incidence and these climatic variables

is imperative to provide the health system with early

warning signals. Such insights are crucial for the

timely implementation of effective vector control

activities. Understanding the interplay between

climatic conditions and malaria incidence allows for

the identiﬁcation of potential risk periods. Early

detection of trends in malaria incidence based on

climatic factors enables the health system to imple-

ment preventive measures and targeted interventions

proactively. This proactive approach is instrumental

in curbing the spread of malaria and minimizing its

impact on public health.

In essence, the association between climatic con-

ditions and malaria incidence serves as a valuable tool

for developing predictive models and early warning

systems. By leveraging this knowledge, health au-

thorities can enhance their preparedness and response

strategies, ensuring a more efﬁcient and timely con-

trol of malaria outbreaks.

1.3 Motivation for this Study

The justiﬁcation for conducting this study lies in the

need to comprehensively understand the geospatial

distribution and spatiotemporal clustering of reported

malaria cases over a three-year period spanning recent

years. This investigation aims to provide valuable in-

sights that can inform targeted malaria interventions

and resource allocation, particularly in regions with

high malaria endemicity at the state level. Given the

current context of climate change, examining the cor-

relation between malaria incidence and climatic fac-

tors becomes crucial. This study seeks to contribute

to malaria surveillance efforts by predicting disease

outbreaks in advance. The analysis of such associa-

tions can enhance early warning systems, allowing for

proactive measures to be implemented in regions sus-

ceptible to increased malaria transmission. Further-

more, the evaluation of the existing malaria surveil-

lance system in India is an essential component of this

study. By identifying areas of improvement and reﬁn-

ing the surveillance process, this study aims to con-

tribute to the overall effectiveness of malaria control

measures in India

2 LITERATURE SURVEY

2.1 Background of Malaria

Malaria is a vector-borne disease caused by Plas-

modium parasite transmitted through the bites of in-

fected female Anopheles mosquitoes, which are ac-

tive between dusk and dawn. These mosquitoes serve

as vectors, living organisms capable of transmitting

infectious agents between humans or from animals

to humans. Of the ﬁve Plasmodium species caus-

ing Malaria in humans, P. falciparum and P. vivax

pose the greatest threat, with P. falciparum being

the most lethal. Globally, in 2021, there were ap-

proximately 247 million reported malaria cases and

619,000 malaria-related deaths. Alarmingly, chil-

dren under the age of ﬁve accounted for 67 per-

cent of these global malaria deaths in 2018 (WHO,

2022)(NVBDCP, 2022). The incubation period for

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

160

most malaria cases typically ranges from seven to

thirty days, representing the time between the bite

of an infected Anopheles mosquito and the onset of

initial symptoms. Malaria symptoms can vary from

mild or nonexistent to severe and life-threatening.

Common symptoms include fever, chills, sweating,

headaches, body aches, nausea, vomiting, and gen-

eral malaise. Preventing and reducing malaria trans-

mission primarily rely on two forms of vector control:

insecticide-treated mosquito nets and indoor residual

spraying. Additionally, antimalarial medications play

a crucial role in preventing Malaria. Suspected cases

are conﬁrmed through parasite-based diagnostic test-

ing, which may involve microscopy or a rapid diag-

nostic test. Early diagnosis and treatment are im-

perative to reduce transmission and prevent fatalities

(CDCP, 2020b).

Several risk factors contribute to the prevalence

of Malaria, including climatic and environmental fac-

tors, genetic factors, and population density. Socio-

economic and behavioral risk factors, such as a lack

of knowledge about Malaria and its control, cul-

tural adherence to traditional and ineffective treat-

ments, and entering endemic regions without preven-

tive measures due to poverty or ignorance, also play

a role. Human activities that create mosquito breed-

ing sites, as well as night-time exposure of farmers to

mosquito bites during agricultural work, contribute to

the risk. Additionally, health system-related factors,

such as shortages in human and ﬁnancial resources,

drugs, and equipment, impact the control and man-

agement of Malaria (CDCP, 2020a). Numerous re-

search studies have unequivocally established a sig-

niﬁcant link between vector-borne diseases and envi-

ronmental factors, offering a key to predicting disease

outbreaks and implementing effective control mea-

sures. Coldblooded arthropod vectors, crucial agents

in these diseases, undergo profound impacts due to

temperature ﬂuctuations, inﬂuencing their develop-

ment, behavior, reproduction, and overall population

dynamics. Moreover, the interplay of temperature

with humidity affects pathogen development within

vectors. The signiﬁcance of rainfall and seasonality

in creating breeding grounds for disease vectors can-

not be understated, further emphasizing the diverse

environmental risk factors, including altitude, slope,

soil type, vegetation, and land use/land cover (Gage

et al., 2008).

2.2 Climatic Factors and Malaria

Transmission

The triad of temperature, relative humidity, and pre-

cipitation emerges as pivotal in the context of malaria

transmission, orchestrating spatiotemporal changes in

malaria vectors. Rainfall, humidity, climate seasonal-

ity, and temperature collectively contribute to 70%-

90% of the malaria risk. The transmission of Plas-

modium falciparum, the causative parasite, is intri-

cately tied to temperature thresholds, with limitations

below 16°C – 19°C and above 33°C – 39°C. Relative

humidity plays a multifaceted role, impacting vector

breeding, parasite development, and the spatial diffu-

sion of malaria transmission. Areas with high vege-

tation in close proximity to human habitation become

hotspots for malaria transmission, particularly when

the distance from mosquito breeding sites is less than

2.5 km (Palaniyandi et al., 2017).

2.3 Forests as Malaria Hotspots

Forests emerge as fertile grounds for malaria trans-

mission due to conducive conditions— vegetation

cover, temperature, rainfall, and humidity—favoring

the distribution and survival of malaria vectors. Tribal

populations dwelling in forested areas rely predom-

inantly on indigenous treatments due to factors like

illiteracy, adherence to age-old traditions, and a deep-

seated fear of the external world. The challenges

are compounded by poor communication infrastruc-

ture, particularly during the rainy season, when

mosquito dispersal dynamics are affected by even

slight changes in distances from bodies of water (Kar

et al., 2014).

2.4 Recent Studies and Geographical

Variations

Over the past decade, numerous studies have delved

into the association between malaria incidence and

climatic factors, presenting a nuanced understand-

ing of the temporal dynamics. Investigations in

China, South Africa, Iran, Thailand, Uganda, Burk-

ina Faso, and India have provided valuable insights

into the complex relationships involving meteorolog-

ical factors. The temporal lagged association between

weekly malaria incidence and meteorological factors

in 30 counties in southwest China from 2004 to 2009

is shown in (Zhao et al., 2014). Also, an inves-

tigation of the effect of monthly rainfall variations

on malaria transmission in ﬁve districts of Limpopo

Province of South Africa for the period 1998 to 2017

is presented in (Adeola et al., 2019). Furthermore,

the association of monthly malaria incidence with cli-

matic factors from 2000 to 2012 was studied in Sis-

tan and Baluchestan, Iran (Mohammadkhani et al.,

2019). The association of weekly malaria incidence

with climatic data throughout the country from 2012

An Exploratory Analysis of Malaria and Climatic Factors in India

161

to 2017 in Thailand is presented in (Kotepui et al.,

2018). Temporal relationships between environmen-

tal factors of weekly rainfall, temperature, and en-

hanced vegetation index series and malaria morbidity

over the period January 2010–May 2013 in Uganda

were studied using cross-correlation (Kigozi et al.,

2016). Case studies from Bhutan and Odisha, India,

reveal distinct geographical nuances. Bhutan experi-

ences a rise in P falciparum cases with rainfall, and

the seasonal peak aligns with the monsoon. The P

falciparum cases increased by 0.7% for a one mm

rainfall, while climatic factors (Temperature, Rain-

fall) were not associated with P vivax (Wangdi et al.,

2020). Odisha reported 26.9% of the total malaria

cases (2005-2010) in India, contributing signiﬁcantly

to India’s malaria burden, relying on numerical sim-

ulations using the VECTRI model, showing the peak

of transmission associated with speciﬁc temperature

and rainfall ranges (Singh Parihar et al., 2019).

3 METHODOLOGY

3.1 Research Objectives

1. To analyze the geospatial distribution of reported

malaria cases in India. 2. To explore spatiotemporal

clustering of reported malaria cases in India. 3. To

study the association of spatiotemporal clustering of

reported malaria cases, if any, and potential climatic

factors in India.

3.2 Study Area

India, spanning an expansive area of approximately

3.29 million square kilometers, stands as a vast and

diverse subcontinent. There are 28 states in In-

dia. The geographical features include the towering

Himalayan mountain range in the north, the fertile

Gangetic plains, the arid Thar Desert in the west, and

the extensive coastline along the Arabian Sea and the

Bay of Bengal. The extensive coastline stretches for

7,517 km, and the holiest river, the Ganga or Ganges,

ﬂows for a remarkable 2,510 km. This diverse to-

pography contributes to a wide range of climates and

ecosystems across the country. Geographically, In-

dia can be broadly categorized into four regions: the

plains, mountains, southern peninsula, and the desert.

The eastern and central regions are characterized by

the fertile Indo-Gangetic plains, while the arid Thar

Desert graces the northwest in Rajasthan. Southern

India predominantly features the Deccan plateau, bor-

dered by the Western Ghats and Eastern Ghats moun-

tain ranges along the coastal areas. Additionally, the

Aravallis and Vindhyachal are prominent mountain

ranges in India. India is the ﬁrst-most populous coun-

try globally, with a population of approximately 1.42

billion people.

India’s environmental landscape is characterized

by a tropical climate, creating distinct wet and dry

seasons. The maximum temperature is 40°C to

47.3°C, and the minimum temperature is -4°C to -

1°C . The average annual rainfall is 1635 mm. Mon-

soon rains, typically from June to September, bring

heavy precipitation and contribute signiﬁcantly to the

country’s water resources. The tropical conditions,

with warm temperatures and high humidity, provide

an ideal environment for the proliferation of disease

vectors, particularly mosquitoes. Vegetation ranges

from dense forests in the Western Ghats to arid land-

scapes in Rajasthan, contributing to the biodiversity

of the subcontinent. The tropical climate of India,

with its pronounced wet season during the monsoons,

creates favorable conditions for the transmission of

vector-borne diseases. Malaria, in particular, thrives

in areas with abundant rainfall and warm tempera-

tures. Environmental factors, including temperature,

humidity, and vegetation cover, signiﬁcantly inﬂu-

ence the breeding and survival of disease vectors,

such as mosquitoes.

3.3 Data Collection

Malaria case data for the period of the speciﬁed time-

frame was procured from the records of the ”National

Center for Vector-Borne Diseases Control.” These

records serve as a comprehensive source for under-

standing the distribution of malaria cases over the

speciﬁed timeframe and over all states of India. Addi-

tional datasets were acquired to enhance the contex-

tual understanding of the malaria data. Demographic

information, providing insights into the population

structure and distribution, was sourced from IndiaS-

tat, a reputable data repository. IndiaStat serves as

a valuable resource for climatic data, contributing to

a holistic analysis of the correlation between malaria

cases and climatic factors.

Furthermore, climate records were obtained from

the India Meteorological Department, Pune, a crucial

element in comprehending the environmental factors

inﬂuencing malaria transmission. IndiaStat, serving

as a reference for climate records, emphasizes the re-

liability and accuracy of the data, ensuring a robust

foundation for assessing the climatic conditions dur-

ing the speciﬁed period. The collaborative nature of

data availability on IndiaStat, in association with the

India Meteorological Department, Pune, adds credi-

bility to the climate data used in the study. More-

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

162

over, the public facilities (i.e., number of hospitals

and beds) and the number of beds records state-wise

were obtained from the Ministry of Health and Family

Welfare.

This multifaceted approach ensures a comprehen-

sive dataset that encompasses the health-related as-

pects of malaria and demographic and climatic di-

mensions crucial for a nuanced analysis of the inter-

play between climatic factors and malaria transmis-

sion.

3.4 Data Cleaning and Software Use

Data download was done in Microsoft Ofﬁce 2019

Excel. All data cleaning and data analysis were

done using the Python software version Google Co-

lab. QGIS software desktop version 3.34 was used

to extract the India map shapeﬁle. Data cleaning in-

volves the conversion of raw data into a coherent and

reliable format suitable for analysis. Ensuring a con-

sistent and reproducible approach to data cleaning is

essential. Python software provides an effective plat-

form for performing reproducible data-cleaning tasks.

Following tidy data principles ensures that each vari-

able occupies its designated column, each observation

aligns with a speciﬁc row, and every value is appro-

priately situated. Adhering to these rules facilitates

a more efﬁcient and organized workﬂow within the

Python environment, enhancing the overall data anal-

ysis process.

Secondary data ﬁles of annual state-wise and

monthly malaria case data for the years 2020 to 2022

were read in Python software for their data cleaning.

Only relevant columns were kept, and the columns

were given proper variable names to be used dur-

ing analysis. Then, all datasets for 2020 to 2022

were merged into a single dataset. All variables were

checked for missing values and imputation was car-

ried out. Data on the temperature are in degrees Cel-

sius (°C) and rainfall in mm. The malaria situation

in India from 2020 to 2022 was analyzed using the

number of monthly malaria cases and the population.

The geospatial distribution of reported malaria cases

in India from 2020 to 2022 was analyzed by creating

choropleth maps at the state level. Pearson correlation

analysis was done to study the association between

monthly state-wise malaria cases, monthly statewise

rainfall, and temperature values.

4 DATA ANALYSIS AND RESULTS

In our comprehensive study against malaria in India

over the years 2020 to 2022, a multifaceted analytical

Figure 1: Monthly Average Malaria Cases for 2020-2022.

strategy was applied to gain nuanced insights into the

temporal trends, seasonal patterns, and geospatial dis-

tribution of malaria cases. Section 4.1 provides results

on the ﬁrst objective, that is, to analyze the geospatial

distribution of reported malaria cases in India by the

creation of choropleth maps at state levels. Section

4.2 presents results related to the second objective:

to explore the spatiotemporal clustering of reported

malaria cases in India. The results of the third objec-

tive (to study the association of spatiotemporal clus-

tering of malaria cases with potential climatic factors)

is presented in section 4.3.

4.1 Epidemiological Situation of

Malaria in India During 2020-2022

The epidemiological situation of malaria in India dur-

ing 2020-2022 was analyzed in terms of population,

blood samples examined, total malaria cases, percent-

age of PF malaria cases, and deaths. The average

monthly malaria cases are shown in Figure 1, and data

from NVBDCP is summarized in Table 1.

The number of blood samples examined has in-

creased from 2020 to 2022. The total number of

malaria cases decreased in 2021 but increased again

in 2022. Also, Pf cases and deaths have decreased

over the period of study. The histogram shows India’s

monthly average malaria cases from 2020 to 2022.

Here, we can see malaria at its peak from July to

November. Temporal analysis has provided a nuanced

understanding of malaria cases’ seasonality and tem-

poral trends. The identiﬁcation of peak periods and

variations over the study period contributes to a more

comprehensive grasp of the disease dynamics.

India is divided into different regions; the North-

East region ( Arunachal Pradesh, Assam, Meghalaya,

Manipur, Nagaland, Mizoram, Tripura), East region

An Exploratory Analysis of Malaria and Climatic Factors in India

163

Table 1: Epidemiological situation of malaria in India during 2020-2022.

Year 2020 2021 2022

Population 1396387127 1407563842 1417173173

BSE 97177024 114391977 152083001

Total Cases 186532 161753 176522

Pf Cases 119088 101566 101068

Deaths 93 90 83

BSE: Blood Sampled Examined, Pf: Plasmodium falciparum

Figure 2: State-wise total population density and tribal population in India based on census 2011.

( Odisha, Chhattisgarh, Jharkhand, and West Ben-

gal), East-South region (Telangana, Andhra Pradesh),

South region (Kerla, Tamil-Nadu), South-west re-

gion (Karnataka, Goa), West region (Maharashtra,

Gujrat), Central region (Madhya Pradesh), Middle-

West region (Rajastan), North region (Haryana, Pun-

jab, Delhi, Himachal Pradesh, Uttarakhand, Jammu

Kashmir, Ladakh).

Figure 2 shows how population density and tribal

population were distributed geographically in India in

the year 2011 based on 2011 census data. Mostly,

the states on the eastern and northeastern sides are

characterized by low population density and a high

proportion of the tribal population, and states on the

western and middle western sides are characterized

by higher population density and a lower proportion

of the tribal population.

4.2 Geospatial Distribution of Malaria

Cases at the State Level

In order to analyze the geospatial distribution of re-

ported malaria cases at the state level, choropleth

maps showing the geospatial distribution of the pro-

portion of malaria cases among states were cre-

ated. Figure 3 shows choropleth maps depicting the

geospatial distribution of Malaria cases among states

in India from the year 2020 to 2022. Overall, malaria

cases in the states increased in 2021 and again de-

creased in 2022. Some of the states in the eastern

region of India had higher cases than those in the

non-eastern region. Odisha, Chhattisgarh, Jharkhand,

West Bengal, Maharashtra, and Uttar Pradesh had the

highest malaria cases in this three-year period. Figure

4 shows choropleth maps depicting the geospatial dis-

tribution of Malaria cases/Population ratio among all

states in India from the year 2020 to 2022. Overall,

the ratio in India has increased. Mizoram, Tripura,

Odisha, Chhattisgarh, Uttarakhand, and Jharkhand

have the highest ratio values.

4.3 Association of Spatiotemporal

Clustering of Malaria Cases with

Climatic Factors

To study the association of spatiotemporal clustering

of reported malaria cases with climatic factors, we

have considered monthly malaria cases and climatic

variables, namely monthly rainfall (in mm) and tem-

perature (in °C ) for various states of India. Rainy

season in India is usually from June to October. It

was observed that there was a peak in malaria cases in

July, August, and September, and most of the malaria

cases were reported from July to October each year

from 2020 to 2022. Thus, malaria cases in India fol-

low seasonal patterns. Figure 5 shows choropleth

maps depicting the geospatial distribution of annual

rainfall among all states in India from 2020 to 2022.

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

164

Figure 3: Total malaria cases in India from 2020 to 2022.

Figure 4: Malaria cases/Population ratio in India from 2020 to 2022.

Figure 5: Annual Rainfall in India from 2020 to 2022.

Figure 6: Annual Average Temperature in India from 2020 to 2022.

An Exploratory Analysis of Malaria and Climatic Factors in India

165

Figure 7: Monthly average malaria cases and rainfall.

The overall rainfall pattern was high in 2021. Also,

the south-region and northeast regions had higher

rainfall records. Figure 6 shows choropleth maps de-

picting the geospatial distribution of annual average

temperature among all states in India from 2020 to

2022. The overall temperature has increased by ap-

proximately 0.3 °C. The South-region states have a

27 °C to 28 °C average temperature, the North-East

region has a 24 °C to 25 °C average temperature,

the west region has 25 °C to 27 °C average temper-

ature, and the north region has 23 °C to 24 °C (except

Jammu and Kashmir, it has 14 °C to 15 °C). Summary

statistics of monthly state-wise malaria cases and me-

teorological variables in India in the three-year period

from 2020 to 2022 are as follows. The temperature

was lowest (-13.6 °C) in the North region (Himachal

Pradesh) in January month of 2020 year and highest

(48.9 °C) in the Middle-west region (Rajasthan: Gan-

ganagar) in May month of the year 2020. Monthly

rainfall was highest (1470.9 mm) in July 2020, while

monthly malaria cases were highest (15330 cases) in

September 2020 in the Uttar Pradesh state of India.

India’s diverse climatic conditions, encompassing

distinct summer, winter, and monsoon seasons, con-

tribute to a complex geographical and environmental

tapestry. Figure 7 presents the average malaria cases

along with the monthly average rainfall. The mon-

soon season, prevailing from June to September dis-

plays variations across the country. Desert regions

witness minimal rainfall, while southern parts, partic-

ularly South India, receive substantial precipitation.

The Himalayan region experiences pleasant spring

and autumn seasons.

From April to June, summers in India are charac-

terized by high temperatures, averaging 40 to 49 de-

grees Celsius. Different regions of India showcase a

wide range of climates. Southern states enjoy a pleas-

ant winter season from November to February, with

temperatures ranging between 17 to 20 degrees Cel-

Figure 8: Monthly average malaria cases and temperature.

sius. Western states also experience agreeable winter

climates, while northern states endure freezing tem-

peratures and heavy snowfall due to their proximity

to the Himalayas. Eastern states similarly encounter

extreme cold conditions. Figure 8 presents the av-

erage malaria cases along with the monthly average

temperature. It can be seen that the range of 26 de-

grees Celsius to 29 degrees Celsius is the tempera-

ture for peak malaria cases. Leveraging geograph-

ical information systems (GIS), geospatial distribu-

tion mapping became a powerful tool to visualize the

spatial patterns of malaria occurrences across differ-

ent regions in India. Identifying hotspots and areas

with higher disease prevalence contributed to a spa-

tially informed understanding of malaria dynamics.

This geospatial perspective provided crucial insights

for targeted interventions, including resource alloca-

tion and region-speciﬁc control measures. Addition-

ally, examining the correlation between malaria cases

and meteorological factors allowed for a deeper un-

derstanding of dynamics of malaria. The summary

statistics of these Pearson correlation coefﬁcients are

given in Table 2, highlighting the signiﬁcance of these

associations.

Table 2: Correlation Coefﬁcient Between Monthly Malaria

Cases and Meteorological Data.

Year Monthly Rainfall Temperature (in °C)

(in mm)

2020 0.73* NA

2021 0.70* 0.53

2022 0.83* 0.48

Note: In Table 2 * means p-value < 0.05, and NA

means temperature data was not available.

Table 2 shows that the correlation coefﬁcient be-

tween monthly malaria cases and rainfall was high

and signiﬁcant compared to the correlation coefﬁ-

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

166

Figure 9: Deaths due to Malaria in India from 2020 to 2022.

cient between monthly malaria cases and temperature.

A positive correlation was found between monthly

malaria cases and rainfall, while no signiﬁcant cor-

relation was observed with temperature.

All of these aspects underscore the importance

of considering spatial and environmental factors in

malaria control strategies.

4.4 Deaths due to Malaria

Figure 9 shows the number of deaths due to Malaria

in India from 2020 to 2022. Maharashtra, Chattis-

garh, Odisha, Jharkhand, West Bengal, Mizoram, and

Meghalaya had higher mortality. The mortality was

at its peak in Maharashtra and Chattisgarh states.

5 DISCUSSION

The results of our study on the spatiotemporal clus-

tering of reported malaria cases in India unveil valu-

able insights into the geographic and temporal dy-

namics of malaria incidence. Geospatial analysis us-

ing Geographic Information System (GIS) tools has

allowed us to map the distribution of reported cases,

revealing areas with signiﬁcant clustering. Our ﬁnd-

ings indicate that certain regions within India exhibit

higher concentrations of malaria cases, emphasizing

the need for targeted interventions in these speciﬁc

geographic hotspots. These interventions may include

intensiﬁed vector control measures, improved health-

care accessibility, and community engagement initia-

tives. Establishing an early warning system based on

the identiﬁed spatiotemporal patterns will enable pub-

lic health authorities to respond promptly to potential

outbreaks, thereby reducing the impact of malaria on

affected communities.

As we move forward, continuous monitoring and

evaluation will be crucial for assessing the effec-

tiveness of implemented interventions and adapting

strategies based on evolving spatiotemporal patterns.

Our study is a vital resource for informing evidence-

based and context-speciﬁc malaria control efforts in

India, supporting public health initiatives with action-

able insights from the comprehensive analysis of re-

ported malaria cases.

REFERENCES

Adeola, A., Ncongwane, K., Abiodun, G., Makgoale, T.,

Rautenbach, H., Botai, J., Adisa, O., and Botai, C.

(2019). Rainfall trends and malaria occurrences in

limpopo province, south africa. International Jour-

nal of Environmental Research and Public Health,

16(24):5156.

CDCP (2020a). Cdc-centers for disease control

and prevention (2019b), about malaria biology.

https://www.cdc.gov/malaria/about/biology/index.html.

CDCP (2020b). Cdc-centers for disease control

and prevention (2019b), about malaria disease.

https://www.cdc.gov/malaria/about/disease.html.

Gage, K. L., Burkot, T. R., Eisen, R. J., and Hayes, E. B.

(2008). Climate and vectorborne diseases. American

journal of preventive medicine, 35(5):436–450.

Kar, N. P., Kumar, A., Singh, O. P., Carlton, J. M., and

Nanda, N. (2014). A review of malaria transmission

dynamics in forest ecosystems. Parasites & vectors,

7:1–12.

Kigozi, R., Zinszer, K., Mpimbaza, A., Sserwanga, A.,

Kigozi, S. P., and Kamya, M. (2016). Assessing tem-

poral associations between environmental factors and

malaria morbidity at varying transmission settings in

uganda. Malaria journal, 15:1–6.

Kotepui, M., Kotepui, K. U., et al. (2018). Impact of

weekly climatic variables on weekly malaria inci-

dence throughout thailand: a country-based six-year

retrospective study. Journal of Environmental and

Public Health, 2018.

Mohammadkhani, M., Khanjani, N., Bakhtiari, B.,

Tabatabai, S. M., and Sheikhzadeh, K. (2019). The

relation between climatic factors and malaria inci-

An Exploratory Analysis of Malaria and Climatic Factors in India

167

dence in sistan and baluchestan, iran. Sage Open,

9(3):2158244019864205.

NVBDCP (2022). National vector borne disease con-

trol programme (nvbdcp), 2020-2022. https://ncvbdc.

mohfw.gov.in/.

Palaniyandi, M., Anand, P., and Pavendar, T. (2017). En-

vironmental risk factors in relation to occurrence of

vector borne disease epidemics: Remote sensing and

gis for rapid assessment, picturesque, and monitoring

towards sustainable health. International Journal of

Mosquito Research, 4(3):9–20.

Saxena, R., Nagpal, B., Srivastava, A., Gupta, S., and

Dash, A. (2009). Application of spatial technology

in malaria research & control: some new insights. In-

dian Journal of Medical Research, 130(2):125–132.

Singh Parihar, R., Bal, P. K., Kumar, V., Mishra, S. K.,

Sahany, S., Salunke, P., Dash, S. K., and Dhiman,

R. C. (2019). Numerical modeling of the dynamics

of malaria transmission in a highly endemic region of

india. Scientiﬁc Reports, 9(1):11903.

Srivastava, A., Nagpal, B., Joshi, P., Paliwal, J., and Dash,

A. (2009). Identiﬁcation of malaria hot spots for fo-

cused intervention in tribal state of india: a gis based

approach. International Journal of Health Geograph-

ics, 8:1–8.

Wangdi, K., Xu, Z., Suwannatrai, A. T., Kurscheid, J., Lal,

A., Namgay, R., Glass, K., Gray, D. J., and Clements,

A. C. (2020). A spatio-temporal analysis to identify

the drivers of malaria transmission in bhutan. Scien-

tiﬁc Reports, 10(1):7060.

WHO (2022). World malaria report 2022. https:

//www.who.int/teams/global-malaria-programme/

reports/world-malaria-report-2022.

Zhao, X., Chen, F., Feng, Z., Li, X., and Zhou, X.-H.

(2014). The temporal lagged association between

meteorological factors and malaria in 30 counties in

south-west china: a multilevel distributed lag non-

linear analysis. Malaria journal, 13:1–12.

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

168