July 9, 2020
In January 2016 in Kenya, the conditions were just right for an outbreak of Rift Valley fever. A strong El Niño on the other side of the world had brought higher temperatures and a wetter-than-normal rainy season to eastern Africa. Rain-soaked and flooded areas provided favorable habitat for Aedes mcintoshi mosquitoes—the primary carrier of the fever-causing virus—to gather and reproduce.
As eastern Africans know, a Rift Valley fever outbreak is not something to be taken lightly. The symptoms of the virus are usually flu-like fevers, muscle and joint pain, and headaches. In extreme cases, it can lead to blindness, hemorrhagic fever, or even death.
During an El Niño event in 2006-07, outbreaks occurred across eastern Africa. The regional economy lost about $60 million (U.S.) due to livestock losses and international export bans. The disease was transmitted from livestock to humans through the consumption of milk and undercooked meat. Outbreaks in Kenya, Sudan, Tanzania, Somalia, and Madagascar killed roughly 500 people and resulted in more than 200,000 human infections.
But in 2016, despite prime mosquito breeding conditions, no outbreak of Rift Valley fever occurred in Kenya. Using satellite data, NASA researchers observed the wetter-than-usual conditions and shared their insights about a potential outbreak with colleagues in international health organizations and African governments. While there is no current vaccine for humans, there is a vaccine for livestock. In response to the alert, Kenyan officials undertook a mass vaccination program for domestic livestock. Despite large numbers of disease-carrying mosquitoes in Kenya, no human or animal outbreaks were reported. Meanwhile, Rift Valley fever outbreaks occurred in Mauritania, Niger, and Uganda.
The prevention of Rift Valley fever in Kenya in 2016 was a success story decades in the making. Twenty years ago, predicting a disease outbreak seemed like the realm of science fiction. But today, researchers can predict when and where certain diseases will spread—from weeks to months beforehand—with the help of satellite data.
“You have to determine where there are disease-favoring conditions in the environment, and you can only determine that by a global surveillance system, which satellites provide,” said Assaf Anyamba, a remote sensing scientist with the NASA Goddard Earth Sciences & Technology Center and leader of the Rift Valley fever monitoring project.
Anyamba’s team is one of several research groups worldwide that are developing predictive outbreak models for some of the world’s most common and neglected (under-studied) diseases—many of which do not have a vaccine. Rift Valley fever, cholera, chikungunya, and dengue are generally found in tropical regions around the world with poor water quality and sanitation, as well as limited access to health care services. Each disease is also connected to the environment.
As ever-more advanced satellites have been launched, disease researchers have acquired better tools to observe the temperature, precipitation, and vegetation conditions that are linked with certain diseases. Scientists have incorporated these data into models that assess the likelihood of disease outbreaks. By anticipating when and where conditions for these diseases might become favorable, researchers can help local governments and international health organizations focus their resources to mitigate outbreaks.
Between 2004 and 2006, islands in the western Indian Ocean experienced an unprecedented outbreak of chikungunya. At least 200,000 people on the island of La Reunion were struck by the disease. This outbreak was preceded by unusually dry and warm weather.
In October 2016, a large outbreak of cholera cases occurred in Haiti following Hurricane Matthew. The category 4 storm caused extensive flooding, damaged infrastructure, and brought a noticeable surge of cholera cases immediately following the destruction.
In 2016, the Zika virus became a pandemic, spreading to more than thirty countries and territories in the Americas, including the United States. The majority of U.S. cases were traced back to travelers who had been to South America, where the virus-carrying mosquitoes thrived after a season of heavy rain and warm temperatures.
There is rarely a simple answer for why a disease outbreak occurs. Usually it is a combination of factors, including the presence of vectors, weather and climate changes, local sanitation practices, lack of immunity in the population, and unpredictable human behavior.
“Disease risk models involve a mixture of understanding of the biology of the organism that spreads the disease and of climate dynamics, plus the numerical tools to be able to put it all together,” said Anyamba, who works with disease experts, biologists, and health officials across organizations and government agencies.
NASA satellites provide one of those model inputs: data on weather and climate that scientists can use to monitor the conditions that precede an outbreak. But how do researchers know which environmental conditions will play a significant part? To answer that question, researchers study past outbreaks.
Long before he made a prediction about the 2016 outbreak, Anyamba studied Rift Valley fever outbreaks dating back to the 1950s in collaboration with entomologist Kenneth Linthicum of the U.S. Agricultural Research Service. Out of roughly a dozen outbreaks across seven decades, they found that all followed periods of heavy rain that were typically brought on by El Niño events or La Niña events. (El Niño brings RVF outbreaks to eastern Africa; La Niña makes them more likely in southern Africa.)
Heavy rain is favorable for the growth and breeding of specific Aedes and Culex mosquitoes that can carry the virus. The female mosquitoes can transmit the virus to their offspring through eggs, which can remain dormant and infectious during dry conditions for many years. Heavy rain allows more of those eggs to hatch.
In fact, many tropical diseases are transmitted by such “vectors”—organisms that do not cause the disease, but spread it to other animals. Chikungunya, dengue fever, and Zika are transmitted through viruses carried predominantly by Aedes aegypti mosquitoes. Cholera is often carried and spread by small crustaceans called copepods. Malaria is spread through the bite of female Anopheles mosquitoes infected with the Plasmodium parasite. Plague is transmitted through fleas—usually living on rodents—that carry the bacteria.
Although satellites cannot track these tiny vectors, they can monitor the environmental conditions that allow the vectors to thrive.
In 2011, hydrologist Antar Jutla began a quest to understand why cholera outbreaks occur. Cholera is a waterborne bacterial infection that can spread quickly through a population. The disease is primarily contracted by consuming water or food contaminated with the cholera bacteria, Vibrio cholerae. It causes uncontrollable diarrhea that, if left untreated, can result in dehydration or death.
In the 1960s, University of Maryland biologist Rita Colwell made the discovery that Vibrio cholerae can live in the guts of microscopic aquatic animals called copepods. Further investigations showed that disease-carrying copepods could lurk in the coastal ocean for months to years. Other research showed that when the right environmental conditions arise, Vibrio cholerae can show up in water supplies and spread disease to people. But at the time researchers did not know which environmental factors brought the bacteria-carrying copepods into water supplies.
As a postdoctoral researcher at the National Oceanic and Atmospheric Administration (NOAA), Jutla worked with Colwell to analyze nearly three decades of cholera outbreak data from India and Bangladesh. They combined long-term health data with satellite and ground-based data of air temperatures, ocean temperatures, salinity, and precipitation. The team also used a ten-year dataset from NASA’s Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) to map and analyze chlorophyll concentrations in nearby waters since plant-like phytoplankton are the primary food source for copepods. Then they ran a computer model that integrated the satellite observations with local information on sanitation, clean-water infrastructure, and populations to retrospectively predict the cholera outbreaks.
The team found that cholera outbreaks took two different forms: endemic and epidemic. Endemic disease means there is a cyclical or seasonal recurrence because the bacteria is constantly present in the environment, even if at a low level. The endemic form of cholera occurs during the dry season, when freshwater river levels are low and saltwater can more easily penetrate into coastal areas. The extra salt provides a good habitat for the growth of algae, which draws in copepods that feed on the floating vegetation. This brings copepods closer to sources of water used for drinking, sanitation, and bathing.
The second form—the epidemic mode—occurs suddenly and sporadically. These outbreaks typically occur after a disaster (such as flooding) contaminates clean water sources or damages water infrastructure. They also alter normal human behavior; for instance, causing populations to relocate suddenly. These damages and disruptions often increase the chance that the infectious bacteria will enter and contaminate local water sources. Epidemic episodes tend to result in more deaths than endemic episodes because the population is often not prepared for the sudden outbreak, which subsequently leads to massive infection rates.
“Bangladesh was a testing ground for our model, the place where we proved we could forecast cholera outbreaks,” said Jutla, who is funded by the NASA Applied Sciences Program and now works on his disease models from the University of Florida. In a proof-of-concept study published in 2013, Jutla and colleagues showed that they could predict both types of outbreaks months in advance for the Bengal Delta. The information was shared with the International Center for Diarrheal Disease Research, which was interested in forecasting the risk of cholera for health professionals in Bangladesh.
The Cholera Prediction Modeling System had a crucial first real-world test in 2017 when the team successfully applied it in another region. Observing heavy rain and warm temperatures, the team predicted that a cholera outbreak could occur in Yemen that summer. However, the team had concerns about the accuracy of their model because they could not get reliable health data from the war-torn country. They made their prediction privately, but were not able to share it with Yemeni officials.
Yemen proceeded to have a devastating cholera outbreak that June—one of its worst in decades—following a mass movement of the population due to civil unrest and heavy flooding. Over four months, more than one million people in Yemen were suspected of having cholera and at least 2,000 died.
Jutla’s model achieved 92 percent accuracy in predicting areas where cholera appeared in Yemen that year. It even identified outbreaks in inland areas that are not usually susceptible to the disease. Following that successful prediction, Jutla and colleagues partnered with UNICEF and the UK Department for International Development to share weekly reports on potential outbreaks in Yemen in order to guide the delivery of prevention and relief resources.
“This 2017 outbreak was a rare occurrence in Yemen, but the model was able to forecast it,” said John Haynes, a program manager in NASA’s Earth Applied Sciences Program. “It was a really big breakthrough to learn that the model could be moved to different parts of the world and still work well.”
The team has since run its cholera prediction model in other parts of the world. For instance, when Cyclone Idai hit Mozambique in March 2019, Jutla’s group was able to predict cholera outbreaks four weeks in advance for various cities. The team has also tested its model in Zimbabwe, Algeria, and Sudan, with plans to cover more of Africa since it is the site of two-thirds of all cholera outbreaks in the world. Jutla and Colwell have a vision to develop a global cholera prediction model in order to reduce the number of cases worldwide.
In 1952, a new and unusual virus was reported in southern Tanzania on the Makonde Plateau. The viral illness caused sudden fever, rash, and joint pain that could last for months. Sometimes causing victims to hunch over due to the aches, the virus was called chikungunya—meaning “bent over in pain” in the local Makonde language. The virus is rarely fatal, but symptoms can last for a few days. It has no vaccine or antiviral treatment.
For the next 50 years, though, chikungunya was largely dormant, with only a few small outbreaks and sporadic cases. But then it made a large-scale resurgence in 2004-2006. At first, it appeared along the Kenyan coast in 2004 in Lamu and then Mombasa. During 2005-2006, the virus spread to hundreds of thousands of people living on western Indian Ocean islands and in India.
As the chikungunya outbreaks were occurring, Anyamba was in the middle of his Rift Valley fever research. But something piqued his interest. Unlike Rift Valley fever, which occurs after heavy rainfall, chikungunya outbreaks were happening during drought conditions.
“Many people think you get rain, then mosquitoes come, and then disease occurs,” he said. “But these large-scale outbreaks illustrated the role of extreme temperatures in influencing these particular vectors.”
Chikungunya is transmitted through the bite of infected Aedes aegypti and Aedes albopictus mosquitoes. The insects gravitate to areas where there is open water for laying their eggs. During a drought, they will leave dry rural areas and move towards urban areas, where open water can be found in water storage containers, flower pots, or small puddles. High temperatures also agitate the insects and cause them to bite more, increasing transmission of the disease.
“During extreme high temperature and drought conditions, you basically change the configuration of habitats for these mosquitoes,” said Anyamba.
Anyamba has since studied various chikungunya outbreaks alongside his Rift Valley fever research. In a span of a decade, chikungunya spread from Asia and Africa to Europe and, most recently, the Americas. In 2015, chikungunya became national news with almost 900 reported cases in the U.S.
Since 2016, Anyamba and his team have been working on a chikungunya risk mapping and forecasting system called CHIKRisk. The model incorporates air temperature and rainfall data from NOAA models; land surface temperatures from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments; humidity and soil moisture data from NASA’s Global Land Data Assimilation System; human population density data from NASA’s Socioeconomic Data and Applications Center; and chikungunya vector distributions from the Walter Reed Biosystematics Unit’s VectorMap. The model is correlated with ground-based surveillance systems (such as the Program for Monitoring Emerging Diseases, or ProMED) that identify disease activity. The project is partly supported by the U.S. Defense Threat Reduction Agency and the NOAA Climate Program Office.
The CHIKRisk model provides monthly outlooks of where chikungunya risk is highest around the world. That information is used by the U.S. Department of Defense’s Global Emerging Infections Surveillance (GEIS) system for situational awareness and health protection for soldiers stationed overseas and by the Pan American Health Organization (PAHO) to help control cases in high-risk areas.
Aedes aegypti and Aedes albopictus mosquitoes are notable vectors in the disease world because of their diversity and ubiquity. Not only do they carry chikungunya, but they also carry dengue fever and Zika. From a modeling perspective, the prediction parameters should be similar for all three diseases. Theoretically, forecast maps could cover multiple diseases for a single location if they have similar environmental influences or common vectors.
During the Zika epidemic of 2015-2016, researchers were able to use what they knew about the mosquitoes to create a predictive model. “We knew a lot about the ecology of Zika because we already knew about its vector,” said Janey Messina, a researcher from the University of Oxford. “To understand the Zika outbreak, we compared it to past dengue outbreaks in Africa by looking at common covariates.”
“When people go into hospitals, their symptoms may be related to many diseases. Until they perform the right test on you, they cannot confirm the disease,” said Anyamba. “Our hope is to make disease forecast maps that could tell public health practitioners that they should be looking for chikungunya, dengue, and Zika.”
Over the years of disease forecasting research, one theme persists: researchers are seeing new diseases in new places. As global climate changes, some diseases are popping up more often, some are spreading faster, and some are occurring under unusual circumstances. In just the past decade, cholera resurfaced in Yemen, West Nile Virus reemerged in the U.S., Zika became a pandemic, dengue fever was reported for the first time in France, and countless other diseases made unlikely appearances.
These increased incidences are not necessarily surprising. The Fifth Intergovernmental Panel on Climate Change (IPCC) report predicted more rainfall, stronger hurricanes, rising temperatures, and more intense heat waves worldwide—all of which could create more favorable habitats for disease vectors.
“The climate has already changed a lot since the 1940s, when a lot of these arboviruses (carried by mosquitoes and ticks) were introduced,” said Messina. “Climate change is going to affect disease transmission, and that’s where preparedness is going to make a difference.”
The most rapidly spreading mosquito-borne disease is dengue fever, which increased thirty-fold in global incidence over the past 50 years. In the past two decades, climate conditions have become more suitable for the mosquitoes in some regions. For instance, the World Health Organization reported that Europe is now vulnerable to a possible outbreak.
Messina recently led a study of the global distribution and risk of dengue in 2050 and 2080. The team found that much of the southeastern United States should have suitable environmental conditions for dengue by 2050. Out of all the continents, though, Africa is expected to see the largest increase due to increased rainfall and rising temperatures—particularly in southern Africa and the Sahel. Globally, the team predicts two billion more people will be at risk for dengue in 2080 compared to 2015.
“These arboviruses are definitely not going away,” said Messina. “All you can do is reduce the effects of an outbreak and how bad it can get.” The main approaches for mitigation are controlling or eliminating the vectors, practicing preventative behavior (such as staying away from or discarding pools of water), and spreading awareness to the public.
Disease-tracking research is now entering a new era: practical use by international health and relief teams. NASA’s Health and Air Quality Applications Program, which sponsors an array of disease-tracking efforts, currently has the highest number of projects and partnerships in its 20-year history. Partners include the Centers for Disease Control and Prevention (CDC), the U.S. Agency for International Development, UNICEF, PAHO, as well as state and local health departments. The Intergovernmental Group on Earth Observations (GEO) also connects experts in Earth remote sensing with public health managers and non-governmental organizations.
For their part, Jutla and his team use the Cholera Prediction Modeling System to create a report each week showing the risk of cholera in certain countries of interest. They send their assessment to more than a hundred colleagues in the public health and remote sensing sectors for their situational awareness and potential action. For instance, Jutla ran the model in May 2020 when Cyclone Amphan slammed into eastern India and Bangladesh. The model showed no potential outbreaks: there was plenty of rain, but temperatures were not warm enough. That was good news and additional validation for the model.
Yemen is also on his mind. The country never fully recovered from its 2017 cholera outbreak, which introduced Vibrio cholerae bacteria back into the region. Since then, poor sanitation systems have allowed the bacteria to persist in drinking water. There are more occurrences of cholera now, particularly in coastal regions where salty rivers create conditions for the bacteria to thrive. What seemed like a one-time epidemic is now turning endemic, and Jutla and Colwell are working to understand conditions that allow the infectious bacteria to stick around in a human population.
For Yemen, the team noted that if water sanitation systems are not improved, the number of cases may become so high that cholera may occur seasonally for years. Colwell and Jutla have recently been working with the UK’s Meteorological Office and its Department for International Development to develop protocols to better educate people on how to interpret the model’s results and to reassess prevention and education methods.
“Ultimately, cholera can only be controlled if all the relevant humanitarian agencies work in tandem and synchrony so that we can make this disease a thing of past,” said Jutla.
The same is true for other vector-borne diseases: the diseases seem to be spreading as climate changes, so information sharing is key to prevention and mitigation. In 2020, Anyamba’s team ran its CHIKRisk model and found elevated risk for chikungunya in India, Mexico, Indonesia, Malaysia, and Philippines. And researchers with NASA’s Health and Air Quality Applications Program are working with local health departments in California, New Jersey, Utah, South Dakota, and Louisiana to predict and prepare for cases of West Nile and dengue.
“Incorporating satellite observations in disease models has become an innovative concept for epidemiologists and health officials, and it has proven to be a valuable component in making public health decisions,” said Haynes. “A lot more interest has been generated in this field over the past two decades. The use of satellite data in disease models has gone from being relatively foreign to almost mainstream.”
Mnamo Januari 2016, Kenya ilipata hali nzuri ya kuambukizwa ugonjwa wa kifafa wa Rift Valley. El Niño yenye nguvu upande ule mwingine wa dunia ilileta joto la juu zaidi na msimu wa mvua wenye unyevunyevu kuliko kawaida katika Afrika mashariki. Maeneo yaliyojaa mvua na yaliyofurika yalitoa makao mazuri kwa mbu wa Aedes mcintoshi - mtoaji mkuu wa virusi vinavyosababisha homa - kukusanya na kuzaliana. Kwa mujibu wa ripoti ya shirika la WHO, ugonjwa wa kifafa wa Rift Valley ni jambo ambalo halitakiwi kuchukuliwa kwa uzito. Dalili za virusi hivyo kwa kawaida ni homa kama ya mafua, maumivu ya misuli na viungo, na maumivu ya kichwa. Katika visa vikali, inaweza kusababisha upofu, homa ya kutokwa na damu, au hata kifo. El Niño ya mwaka 2006-07 ilisababisha mlipuko mkubwa katika eneo la Afrika Mashariki. Uchumi wa eneo hilo ulipoteza karibu dola milioni 60 kwa sababu ya hasara za mifugo na marufuku ya kimataifa ya kuuza nje. Ugonjwa huo ulipitishwa kutoka kwa mifugo hadi kwa wanadamu kupitia ulaji wa maziwa na nyama isiyochemshwa vizuri. Mlipuko huo nchini Kenya, Sudan, Tanzania, Somalia na Madagascar uliua watu 500 na kusababisha maambukizi zaidi ya 200,000 kwa wanadamu. Hata hivyo, mwaka 2016, licha ya hali nzuri za kuzaliana kwa mbu, hakuna mlipuko wa homa ya Rift Valley nchini Kenya. Kwa kutumia data za satelaiti, watafiti wa NASA waliona hali ya mvua zaidi ya kawaida na kushiriki maoni yao juu ya mlipuko wa uwezekano na wenzake katika mashirika ya afya ya kimataifa na serikali za Afrika. Ingawa kwa sasa hakuna chanjo kwa ajili ya wanadamu, kuna chanjo kwa ajili ya mifugo. Kwa sababu ya onyo hilo, maofisa wa Kenya walianzisha programu kubwa ya chanjo kwa mifugo ya nyumbani. Licha ya idadi kubwa ya mbu wanaobeba magonjwa nchini Kenya, hakuna milipuko ya magonjwa ya binadamu au wanyama iliyoripotiwa. Wakati huohuo, milipuko ya homa ya Bonde la Rift ilitokea nchini Mauritania, Niger, na Uganda. Ugonjwa wa homa ya Rift Valley nchini Kenya ulisababisha vifo vya watu wengi. Miaka ishirini iliyopita, kutabiri mlipuko wa ugonjwa kulionekana kuwa jambo la kuwaziwa tu katika hadithi za kubuniwa za kisayansi. Lakini leo, watafiti wanaweza kutabiri wakati na mahali ambapo magonjwa fulani yataenea - kuanzia majuma hadi miezi mapema - kwa msaada wa data za satelaiti. "Ni lazima uamue ni wapi kuna hali zinazopendelea ugonjwa katika mazingira, na unaweza tu kuamua kwamba kwa mfumo wa ufuatiliaji wa kimataifa, ambao satelaiti hutoa, ""Assaf Anambaa, mwanasayansi wa uchunguzi wa mbali na NASA Goddard Sayansi ya Dunia na Kituo cha Teknolojia na kiongozi wa mradi wa ufuatiliaji wa homa ya Rift Valley alisema." Timu ya Anyamba ni moja ya vikundi kadhaa vya utafiti ulimwenguni kote ambavyo vinaendeleza mifano ya utabiri wa mlipuko kwa baadhi ya magonjwa ya kawaida na yaliyopuuzwa (yasiyojifunza) ulimwenguni - mengi ambayo hayana chanjo. Homa ya Rift Valley, kipindupindu, chikungunya na homa ya dengue kwa ujumla hupatikana katika maeneo ya kitropiki ulimwenguni kote na ubora duni wa maji na usafi wa mazingira, na pia upatikanaji mdogo wa huduma za huduma za afya. Kila ugonjwa pia unahusiana na mazingira. Kwa kuwa satelaiti za hali ya juu zaidi zimetolewa angani, watafiti wa magonjwa wamepata vifaa bora zaidi vya kuchunguza hali ya joto, mvua, na mimea inayohusiana na magonjwa fulani. Wanasayansi wametumia data hizo katika mifano inayokadiria uwezekano wa kutokea kwa magonjwa. Kwa kutabiri wakati na mahali ambapo hali za magonjwa haya zinaweza kuwa nzuri, watafiti wanaweza kusaidia serikali za mitaa na mashirika ya kimataifa ya afya kukazia rasilimali zao kupunguza milipuko. Katika kipindi cha miaka ya 2004 hadi 2006, visiwa vya Magharibi mwa Bahari ya Hindi vilipata mlipuko wa chikungunya. Watu 200,000 katika kisiwa cha La Reunion waliambukizwa ugonjwa huo. Mlipuko huu ulitangulizwa na hali ya hewa kavu na yenye joto isiyo ya kawaida. Mwezi Oktoba mwaka 2016, Haiti ilipata janga kubwa la ugonjwa wa kipindupindu baada ya kimbunga Matthew. Dhoruba ya kiwango cha nne ilisababisha mafuriko makubwa, kuharibu miundombinu, na kuleta ongezeko la visa vya kipindupindu mara tu baada ya uharibifu huo. Virusi vya Zika vilienea katika nchi zaidi ya 30 katika Amerika, ikiwa ni pamoja na Marekani. Idadi kubwa ya visa vya maambukizi nchini Marekani vimetokana na wasafiri waliotembelea Amerika Kusini, ambapo mbu wanaobeba virusi vya Corona walipata mafanikio makubwa baada ya msimu wa mvua kubwa na joto kali. Ni mara chache sana kuna jibu rahisi la sababu ya kuzuka kwa ugonjwa. Kwa kawaida ni mchanganyiko wa mambo, ikiwa ni pamoja na uwepo wa vektor, mabadiliko ya hali ya hewa na hali ya hewa, mazoea ya usafi wa mazingira, ukosefu wa kinga katika idadi ya watu, na tabia isiyotabirika ya binadamu. "Mifano ya hatari ya magonjwa inahusisha mchanganyiko wa uelewa wa biolojia ya kiumbe kinachoeneza ugonjwa na mienendo ya hali ya hewa, pamoja na zana za hesabu kuweza kuunganisha yote pamoja, ""alisema Anyamba, ambaye anafanya kazi na wataalamu wa magonjwa, wanabiolojia, na maafisa wa afya katika mashirika na mashirika ya serikali." Satellite za NASA hutoa moja ya vipengele vya mfano: data juu ya hali ya hewa na hali ya hewa ambayo wanasayansi wanaweza kutumia kufuatilia hali ambazo zinaongoza mlipuko. Lakini watafiti wanajuaje ni hali zipi za mazingira zitakazochangia jambo hilo? Ili kujibu swali hilo, watafiti huchunguza milipuko ya zamani. Anambaa aliandika kitabu chake cha kwanza cha utafiti kuhusu ugonjwa wa kifafa wa Rift Valley mnamo mwaka wa 1950 na kuchapishwa na Kenneth Linthicum, mtaalamu wa wadudu wa Marekani. Huduma ya Utafiti wa Kilimo. Katika kipindi cha miaka saba iliyopita, mlipuko wa El Niño umekuwa ukifuatwa na mvua kubwa na matukio ya kawaida ya La Niña. El Niño ilisababisha mlipuko wa virusi vya corona katika Afrika ya Mashariki, La Niña ilifanya uwezekano wa mlipuko huo kuwa mkubwa zaidi kusini mwa Afrika. Mvua kubwa ni nzuri kwa ukuaji na uzazi wa mbu maalum wa Aedes na Culex ambao wanaweza kubeba virusi. Mbu wa kike huweza kuambukiza virusi kwa watoto wao kupitia mayai, ambayo yanaweza kubaki yasiyo na virusi wakati wa hali kavu kwa miaka mingi. Mvua kubwa huruhusu mayai hayo mengi yaanguke. Kwa kweli, magonjwa mengi ya kitropiki husambazwa na viumbe kama hivyo "vector" ambao hawaisababishi ugonjwa huo, lakini hueneza kwa wanyama wengine. Chikungunya, homa ya dengue, na Zika husambazwa kupitia virusi vinavyosambazwa hasa na mbu aina ya Aedes aegypti. Mara nyingi ugonjwa wa kipindupindu husambazwa na wanyama wadogo wanaoitwa copepods. Malaria huenezwa kupitia kuumwa na mbu wa kike wa Anopheles walioambukizwa vimelea vya Plasmodium. Tauni husambazwa kupitia chawa - kwa kawaida wanaoishi kwenye panya - ambao hubeba bakteria. Ingawa satelaiti haziwezi kufuatilia vektor hizo ndogo-ndogo, zinaweza kuchunguza hali za mazingira zinazoruhusu vektor hizo kusitawi. Katika mwaka wa 2011, mtaalamu wa maji Antar Jutla alianza kutafuta sababu za kuzuka kwa ugonjwa wa kipindupindu. Kole ni maambukizo ya bakteria yanayopitishwa na maji ambayo yanaweza kuenea haraka katika idadi ya watu. Ugonjwa huo hupatikana hasa kwa kula maji au chakula kilichochafuliwa na bakteria ya kolera, Vibrio cholerae. Husababisha kuhara kwa njia isiyoweza kudhibitiwa ambayo, ikiwa haitashughulikiwa, yaweza kusababisha upungufu wa maji mwilini au kifo. Katika miaka ya 1960, mwanabiolojia wa Chuo Kikuu cha Maryland, Rita Colwell, aligundua kwamba Vibrio cholerae inaweza kuishi katika matumbo ya wanyama wadogo wa majini wanaoitwa copepods. Uchunguzi zaidi ulionyesha kwamba copepods zinazobeba magonjwa zingeweza kunyemelea katika bahari ya pwani kwa miezi hadi miaka. Utafiti mwingine ulionyesha kwamba wakati hali sahihi za mazingira zinapoibuka, Vibrio cholerae inaweza kuonekana katika maji na kueneza ugonjwa kwa watu. Hata hivyo, wakati huo watafiti hawakujua ni mambo gani ya mazingira yaliyowasababisha wadudu hao wa aina ya copepod kuingia kwenye maji. Akiwa mtafiti wa postdoctoral katika Usimamizi wa Taifa wa Bahari na Anga (NOAA), Jutla alifanya kazi na Colwell kuchambua karibu miongo mitatu ya data ya mlipuko wa kipindupindu kutoka India na Bangladesh. Walichanganya data za afya za muda mrefu na data za satelaiti na za ardhini za joto la hewa, joto la bahari, chumvi, na mvua. Timu hiyo pia ilitumia seti ya data ya miaka kumi kutoka kwa NASA's Sea Viewing Wide Field of View Sensor (SWFS) kuhariri na kuchambua viwango vya klorofili katika maji ya karibu, kwani phytoplankton kama mimea ni chanzo kikuu cha chakula kwa copepods. Kisha wakafanya utafiti wa kompyuta na kutumia data kutoka kwa satelaiti na habari za eneo hilo kuhusu usafi wa mazingira, maji safi, na idadi ya watu ili kutabiri uwezekano wa kutokea kwa ugonjwa huo. Timu hiyo iligundua kwamba milipuko ya kipindupindu ilikuwa na aina mbili tofauti: ya kienyeji na ya tauni. Ugonjwa wa kawaida ni ugonjwa unaotokea mara kwa mara kwa sababu bakteria iko katika mazingira, hata ikiwa ni ndogo. Aina ya ugonjwa wa kipindupindu huenea wakati wa majira ya kiangazi, wakati ambapo maji ya mito ni ya chini na maji ya chumvi yanaweza kuingia kwa urahisi katika maeneo ya pwani. Chumvi hiyo ya ziada hutoa makao mazuri kwa ajili ya ukuzi wa mwani, ambao huvutia viumbe wanaoitwa copepods ambao hula mimea inayoelea. Hilo huwafanya copepods wawe karibu zaidi na vyanzo vya maji yanayotumiwa kwa ajili ya kunywa, usafi wa mazingira, na kuoga. Aina ya pili - hali ya janga - hutokea ghafula na mara kwa mara. Mlipuko huu hutokea baada ya msiba (kama vile mafuriko) kuchafua vyanzo vya maji safi au kuharibu miundombinu ya maji. Pia hubadili tabia ya kawaida ya kibinadamu; kwa mfano, husababisha idadi ya watu kuhamia mahali pengine ghafula. Uharibifu huo na usumbufu huo mara nyingi huongeza uwezekano wa bakteria zenye kuambukiza kuingia na kuchafua vyanzo vya maji vya mahali hapo. Matukio ya janga huwa na matokeo ya vifo zaidi kuliko matukio ya kawaida kwa sababu idadi ya watu mara nyingi haijatayarishwa kwa mlipuko wa ghafla, ambayo baadaye husababisha viwango vikubwa vya maambukizi. "Bangladesh ilikuwa uwanja wa majaribio kwa mfano wetu, mahali ambapo tulithibitisha tunaweza kutabiri milipuko ya kipindupindu, "alisema Jutla, ambaye ni kufadhiliwa na Programu ya Sayansi ya NASA na sasa anafanya kazi kwenye mifano yake ya ugonjwa kutoka Chuo Kikuu cha Florida." Katika utafiti uliochapishwa mwaka 2013, Jutla na wenzake walionyesha kuwa wanaweza kutabiri aina zote mbili za milipuko ya ugonjwa wa corona miezi kadhaa mapema katika Delta ya Bengal. Taarifa hiyo ilishirikiwa na Kituo cha Kimataifa cha Utafiti wa Magonjwa ya Kuvuja, ambacho kilikuwa na nia ya kutabiri hatari ya kipindupindu kwa wataalamu wa afya nchini Bangladesh. Cholera Prediction Modeling System (CPS) ilipata majaribio ya kwanza ya ulimwengu halisi mnamo 2017 wakati timu ilipofanikiwa kuitumia katika mkoa mwingine. Kwa kuchunguza mvua kubwa na halijoto ya joto, kikundi hicho kilitabiri kwamba mlipuko wa kipindupindu ungeweza kutokea nchini Yemen wakati huo wa kiangazi. Hata hivyo, watafiti hao walikuwa na wasiwasi kuhusu usahihi wa mfano wao kwa sababu hawakuweza kupata data ya afya ya kuaminika kutoka nchi hiyo iliyoharibiwa na vita. Walitoa utabiri wao kwa faragha, lakini hawakuweza kuushiriki na maafisa wa Yemen. Yemen iliendelea kuwa na mlipuko mbaya wa kipindupindu mnamo Juni - moja ya mbaya zaidi katika miongo kadhaa - kufuatia harakati kubwa ya idadi ya watu kutokana na machafuko ya kiraia na mafuriko makubwa. Katika kipindi cha miezi minne, zaidi ya watu milioni moja nchini Yemen walishukiwa kuwa na ugonjwa wa kipindupindu na angalau watu 2,000 walikufa. Kwa mfano, Jutla aliweza kutabiri kwa usahihi maeneo ambayo ugonjwa wa kipindupindu ulianza kutokea mwaka huo. Hata iligundua milipuko katika maeneo ya ndani ya nchi ambayo kwa kawaida hayana uwezekano wa kupatwa na ugonjwa huo. Baada ya utabiri huo, Jutla na wenzake walishirikiana na UNICEF na Idara ya Maendeleo ya Kimataifa ya Uingereza kushiriki ripoti za kila wiki juu ya milipuko ya uwezekano nchini Yemen ili kuongoza utoaji wa rasilimali za kuzuia na misaada. "Mlipuko huu wa 2017 ulikuwa tukio nadra nchini Yemen, lakini mfano huo ulikuweza kutabiri, "alisema John Haynes, meneja wa programu katika Programu ya Sayansi ya Sayansi ya NASA ya Sayansi ya Sayansi ya Sayansi ya Sayansi ya Sayansi ya Sayansi ya Sayansi ya Sayansi ya Sayansi." "Ilikuwa ni mafanikio makubwa kujifunza kwamba mfano huo unaweza kuhamishwa kwa sehemu tofauti za ulimwengu na bado unafanya kazi vizuri. "" Timu hiyo tangu wakati huo imeendesha mfano wake wa utabiri wa kipindupindu katika sehemu nyingine za ulimwengu. """ Kwa mfano, wakati kimbunga Idai kilipopiga Msumbiji mnamo Machi 2019, kikundi cha Jutla kiliweza kutabiri milipuko ya kipindupindu wiki nne mapema kwa miji mbalimbali. Timu hiyo pia imejaribu mfano wake nchini Zimbabwe, Algeria na Sudan, na ina mipango ya kufunika sehemu kubwa zaidi ya Afrika kwani ni eneo la thuluthi mbili za milipuko yote ya kipindupindu duniani. Juttla na Colwell wana maono ya kuendeleza mfano wa utabiri wa ugonjwa wa kipindupindu ulimwenguni ili kupunguza idadi ya kesi ulimwenguni. Virusi hivyo viligunduliwa mwaka 1952 katika eneo la Makonde Plateau, Tanzania. Ugonjwa huo wa virusi ulisababisha homa ya ghafula, mlipuko, na maumivu ya viungo ambayo yangeweza kudumu kwa miezi mingi. Wakati mwingine kusababisha waathirika hunch juu kutokana na maumivu, virusi ilikuwa kuitwa chikungunya <unk> maana <unk>bent juu ya maumivu<unk> katika lugha ya ndani Makonde. Virusi hivyo haviwezi kuua, lakini dalili za virusi hivyo zinaweza kudumu kwa siku chache. Hakuna chanjo au matibabu ya kupambana na virusi. Kwa miaka 50 iliyofuata, chikungunya ilibaki kimya, na kulikuwa na milipuko michache tu ya ugonjwa huo. Lakini baada ya hapo, ilibadilika na kuwa kubwa zaidi katika kipindi cha 2004-2006. Mlipuko huo ulifanyika katika eneo la Lamu, Kenya, mwaka 2004 na baadaye katika eneo la Mombasa. Katika kipindi cha mwaka 2005-2006, virusi hivyo vilisambaa kwa mamia ya maelfu ya watu wanaoishi katika visiwa vya magharibi mwa Bahari ya Hindi na India. Wakati wa milipuko ya chikungunya, Anyamba alikuwa katikati ya utafiti wake wa homa ya Rift Valley. Lakini jambo fulani lilimvutia. Tofauti na homa ya Rift Valley, ambayo hutokea baada ya mvua kubwa, milipuko ya chikungunya ilikuwa ikifanyika wakati wa hali ya ukame. "Watu wengi hufikiri unapopata mvua, kisha mbu huja, na kisha ugonjwa hutokea", akasema. "Lakini milipuko hii kubwa ilionyesha jukumu la joto kali katika kuathiri vektor hizi maalum. ""Chikungunya husambazwa kupitia kuumwa na mbu walioambukizwa Aedes aegypti na Aedes albopictus." Wadudu hao hupenda kwenda maeneo yenye maji ya wazi ili waza mayai yao. Wakati wa ukame, wao huondoka maeneo ya mashambani yenye ukame na kuhamia maeneo ya mijini, ambapo maji ya wazi yanaweza kupatikana katika chombo cha kuhifadhi maji, sufuria za maua, au vidimbwi vidogo. Joto la juu pia huchochea wadudu na kuwafanya wachunge zaidi, na hivyo kuongeza kuambukizwa kwa ugonjwa huo. "Wakati wa hali ya joto kali na ukame, kimsingi unabadilisha muundo wa makazi ya mbu hawa", alisema Anyamba. Tangu wakati huo Anyamba amejifunza milipuko mbalimbali ya chikungunya pamoja na utafiti wake wa homa ya Rift Valley. Katika kipindi cha miaka kumi tu, chikungunya ilienea kutoka Asia na Afrika hadi Ulaya na, hivi karibuni zaidi, Amerika. Mwaka 2015, ugonjwa wa chikungunya ulijulikana sana nchini Marekani, na watu 900 waliugua ugonjwa huo. Tangu mwaka 2016, Anyamba na timu yake wamekuwa wakifanya kazi kwenye mfumo wa ramani na utabiri wa hatari ya chikungunya unaoitwa CHIKRISK. Mfano huo unajumuisha data ya joto la hewa na mvua kutoka kwa mifano ya NOAA; joto la uso wa ardhi kutoka kwa vyombo vya NASA vya Moderate Resolution Imaging Spectroscopy (MODIS); unyevu na unyevu wa udongo kutoka kwa Mfumo wa Data ya Dunia ya NASA; data ya wiani wa idadi ya watu kutoka kwa Kituo cha Takwimu na Maombi ya Jamii ya NASA; na usambazaji wa vector ya chikungunya kutoka kwa Walter Reed Biosystems Unit. Mfano huo unalingana na mifumo ya ufuatiliaji wa ardhi (kama vile Programu ya Ufuatiliaji wa Magonjwa ya Kuibuka, au ProMED) ambayo hutambua shughuli za ugonjwa. Mradi huo unaungwa mkono na Shirika la Kupunguza Tisho la Ulinzi la Marekani na Ofisi ya Programu ya Hali ya Hewa ya NOAA. Chini ya mfumo wa CHIKRISK, utafiti wa kila mwezi unaonyesha maeneo ya dunia yenye hatari kubwa ya ugonjwa wa chikungunya. Taarifa hizo hutumiwa na mfumo wa Global Emerging Infections Surveillance (GEIS) wa Wizara ya Ulinzi ya Marekani kwa ajili ya ufahamu wa hali na ulinzi wa afya kwa askari waliowekwa nje ya nchi na na Shirika la Afya la Pan American (PAHO) kusaidia kudhibiti kesi katika maeneo ya hatari. Mbu aina ya Aedes aegypti na Aedes albopictus ni wadudu wa kawaida wa kuambukiza magonjwa ya kuambukiza kwa sababu ya aina zao mbalimbali na kuenea. Kwa sababu ya ugonjwa wa chikungunya, watu wengi wana ugonjwa wa homa ya dengue na Zika. Kutoka mtazamo wa modeling, vigezo utabiri lazima kuwa sawa kwa magonjwa yote matatu. Kwa nadharia, ramani za utabiri zinaweza kufunika magonjwa mengi kwa eneo moja ikiwa yana ushawishi sawa wa mazingira au vektor za kawaida. Wakati wa janga la Zika la mwaka 2015-2016, watafiti waliweza kutumia kile walichokijua kuhusu mbu kuunda mfano wa utabiri. "Tulijua mengi kuhusu ikolojia ya Zika kwa sababu tayari tulijua kuhusu vector yake", alisema Janey Messina, mtafiti kutoka Chuo Kikuu cha Oxford. "Kwa hivyo, kwa sababu ya ugonjwa wa Zika, tumechunguza dalili za ugonjwa wa dengue na kugundua kwamba ""wakati watu wanapokwenda hospitalini, dalili zao zinaweza kuhusiana na magonjwa mengi.""" Mpaka watakapokufanya mtihani sahihi, hawawezi kuthibitisha ugonjwa huo, <unk> alisema Anyamba. "Tuna matumaini ya kutengeneza ramani za utabiri wa magonjwa ambayo inaweza kuwaambia watendaji wa afya ya umma kwamba wanapaswa kuangalia kwa chikungunya, homa ya deni, na Zika. ""Kwa miaka ya utafiti wa utabiri wa magonjwa, mandhari moja inabaki: watafiti wanaona magonjwa mapya katika maeneo mapya." Kwa sababu ya mabadiliko ya hali ya hewa, magonjwa fulani yanatokea mara nyingi zaidi, mengine yanaenea haraka zaidi, na mengine yanatokea katika hali zisizo za kawaida. Katika miaka ya hivi karibuni, ugonjwa wa kipindupindu umeenea nchini Yemen, virusi vya West Nile vimeenea nchini Marekani, ugonjwa wa Zika umekuwa janga, homa ya dengue imeripotiwa kwa mara ya kwanza nchini Ufaransa, na magonjwa mengine mengi yameonekana. Ongezeko hilo la visa si jambo la kushangaza. Ripoti ya tano ya Intergovernmental Panel on Climate Change (IPCC) ilitabiri mvua zaidi, vimbunga vikali zaidi, ongezeko la joto, na mawimbi ya joto zaidi ulimwenguni kote - yote ambayo yanaweza kuunda mazingira mazuri zaidi kwa vektor za magonjwa. "Mabadiliko makubwa ya hali ya hewa yametokea tangu miaka ya 1940 wakati virusi vingi vya arbovirus (vile vinavyosafirishwa na mbu na makapi) vilipoletwa,"" alisema Messina." "Mabadiliko ya hali ya hewa yatathmini kuenea kwa magonjwa, na hapo ndipo utayari utafanya tofauti, ""alisema Dk. Katika miongo miwili iliyopita, hali ya hewa imekuwa nzuri zaidi kwa mbu katika maeneo fulani. Kwa mfano, Shirika la Afya Ulimwenguni liliripoti kwamba sasa Ulaya iko hatarini kwa uwezekano wa mlipuko. Messina ameshiriki katika utafiti wa kuenea kwa homa ya dengue duniani kote mnamo mwaka 2050 na 2080. Watafiti hao walipata kwamba eneo kubwa la kusini-mashariki mwa Marekani linapaswa kuwa na mazingira yanayofaa kwa ajili ya ugonjwa wa dengue kufikia mwaka 2050. Kati ya mabara yote, ingawa, Afrika inatarajiwa kuona ongezeko kubwa zaidi kutokana na ongezeko la mvua na kuongezeka kwa joto - haswa kusini mwa Afrika na Sahel. Kwa mujibu wa ripoti hiyo, watu bilioni mbili zaidi duniani watakuwa katika hatari ya kupatwa na homa ya dengue mwaka 2080 ikilinganishwa na mwaka 2015. "Hii arboviruses ni dhahiri si kwenda mbali", alisema Messina. "Kila kitu unaweza kufanya ni kupunguza athari za mlipuko na jinsi inaweza kuwa mbaya. ""Mkakati mkuu wa kupunguza ni kudhibiti au kuondoa vectors, mazoezi ya tabia ya kuzuia (kama vile kukaa mbali na au kutupa vidimbwi vya maji), na kueneza ufahamu kwa umma." Utafiti wa kufuatilia magonjwa sasa unaingia katika enzi mpya: matumizi ya vitendo na timu za kimataifa za afya na misaada. Programu ya Maombi ya Afya na Ubora wa Hewa ya NASA, ambayo inasaidia juhudi nyingi za kufuatilia magonjwa, kwa sasa ina idadi kubwa zaidi ya miradi na ushirikiano katika historia yake ya miaka 20. Washirika ni pamoja na Vituo vya Kudhibiti na Kuzuia Magonjwa (CDC), Shirika la Marekani la Maendeleo ya Kimataifa (USAID), UNICEF, PAHO, na idara za afya za serikali na za mitaa. Intergovernmental Group on Earth Observations (GEO) pia inaunganisha wataalamu katika Earth remote sensing na wasimamizi wa afya ya umma na mashirika yasiyo ya serikali. Kwa upande wao, Jutla na timu yake hutumia Mfumo wa Utabiri wa Modeling wa Kolera kuunda ripoti kila wiki inayoonyesha hatari ya kikohozi katika nchi fulani za kuvutia. Wanachunguzaji hao wanawasilisha maoni yao kwa zaidi ya wafanyakazi wao 100 katika sekta ya afya ya umma na utambuzi wa mbali ili waweze kujua hali na hatua wanazoweza kuchukua. Kwa mfano, Jutla aliendesha mfano huo mnamo Mei 2020 wakati Kimbunga Amphan kilipopiga India ya mashariki na Bangladesh. Mfano huo haukuonyesha milipuko yoyote inayowezekana: kulikuwa na mvua nyingi, lakini joto halikuwa la kutosha. Hiyo ilikuwa habari nzuri na uthibitisho wa ziada kwa ajili ya mfano. Yemen pia iko akilini mwake. Nchi hiyo haijawahi kupona kabisa kutokana na mlipuko wa kipindupindu wa mwaka 2017, ambao ulirudisha bakteria ya Vibrio cholerae katika eneo hilo. Tangu wakati huo, mifumo mbaya ya usafi wa mazingira imeruhusu bakteria hizo ziendelee kuwa katika maji ya kunywa. Mlipuko wa ugonjwa wa kipindupindu unaongezeka hasa katika maeneo ya pwani ambapo mito yenye chumvi huwezesha bakteria kuenea. Ugonjwa huo ambao ulionekana kuwa wa mara moja sasa unazidi kuenea, na Jutla na Colwell wanafanya kazi ya kuelewa hali zinazoruhusu bakteria hiyo kuambukiza kuenea katika jamii ya wanadamu. Kwa Yemen, timu hiyo ilibainisha kwamba ikiwa mifumo ya usafi wa maji haiboreshwa, idadi ya visa inaweza kuwa kubwa sana hivi kwamba ugonjwa wa kipindupindu unaweza kutokea kwa msimu kwa miaka mingi. Colwell na Jutla wamekuwa wakifanya kazi na Ofisi ya Hali ya Hewa ya Uingereza na Idara yake ya Maendeleo ya Kimataifa ili kuendeleza itifaki za kuwaelimisha watu vizuri juu ya jinsi ya kutafsiri matokeo ya mfano na kupima mbinu za kuzuia na elimu. "Mwishowe, ugonjwa wa kipindupindu unaweza kudhibitiwa tu ikiwa mashirika yote ya kibinadamu yanahusika hufanya kazi kwa pamoja na kwa usawa ili tuweze kufanya ugonjwa huu kuwa kitu cha zamani", alisema Jutla. "Hiyo ni kweli kwa magonjwa mengine yanayopitishwa na vector: magonjwa yanaonekana kuenea kama mabadiliko ya hali ya hewa, kwa hivyo kushiriki habari ni ufunguo wa kuzuia na kupunguza.""" Katika mwaka 2020, timu ya Anambra ilifanya utafiti wa CHIKRISK na kugundua hatari kubwa ya chikungunya nchini India, Mexico, Indonesia, Malaysia na Ufilipino. Watafiti wa NASA na Idara ya Afya ya New Jersey, Utah, South Dakota na Louisiana wanafanya kazi na idara za afya za mitaa kutabiri na kujitayarisha kwa kesi za West Nile na homa ya dengue. "Kuingiza uchunguzi wa satelaiti katika mifano ya magonjwa imekuwa dhana ya ubunifu kwa wataalamu wa magonjwa na maafisa wa afya, na imethibitika kuwa sehemu muhimu katika kufanya maamuzi ya afya ya umma", alisema Haynes. <unk> Maslahi mengi zaidi yameundwa katika uwanja huu katika miongo miwili iliyopita. Matumizi ya data ya satelaiti katika mifano ya magonjwa yametoka kuwa ya kigeni hadi karibu ya kawaida. <unk>