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LIRAa is repeated three to four times per year, with a national survey conducted every October. For the purposes of this analysis we used data from the January surveillance of both programs—immediately preceding the early-spring rainy season characteristic of NE Brazil—that is considered the most valuable for forecasting regional epidemic risk. In , the January LIRAa survey was delayed until February, resulting in elevated bairro infestation indexes relative to the other years; as a result, we designated LIRAa results as missing, and used a missingness indicator variable [ 69 ].

Under the assumption of representative sampling, we used total houses sampled by bairro as a denominator to create a bairro -level household infestation variable for each year. We also used data from surveillance of strategic points SP —locations identified by the municipality as higher risk for harboring Aedes breeding sites—to assess the epidemiological relevance of Aedes infestation at targeted non-residential locations.

In Brazil, SP are a central component of national directives for vector control to prevent dengue. Municipal vector control operations agents are tasked with creating a roster of sites likely to be susceptible to mosquito oviposition and inspecting those sites every 15 days.

These operations are coordinated locally, such that each municipality is responsible for its own roster of strategic points [ 62 ]. Vector control teams inspect SPs in day cycles and code the visit as positive or negative for the presence of Aedes larvae or pupae. We selected the buffer size using two criteria: i expected Aedes mosquito flight distance in an urban setting [ 10 , 16 , 17 ]; and ii buffer size used by the Brazilian Ministry of Health to define the transmission block area around identified vector oviposition sites [ 62 ].

For the positivity variable, bairros without an SP visit during an inspection cycle were coded as missing with a missingness indicator variable. The proximity variable was calculated in ArcGIS. Cycles are numbered sequentially, starting with Cycle 1 for the first 15 days of the year. Daily precipitation estimates were aggregated to weeks, and spatially joined to bairros. Efforts to associate precipitation volume with dengue incidence must incorporate a time lag to adjust for vector development. While lags of zero [ 45 ], one [ 71 , 72 ], and two months [ 37 , 73 ] were reported, the mechanistic role of precipitation in dengue transmission can be confounded by water storage behavior [ 43 ], and with the potential for heavy rains to wash out exposed oviposition sites [ 74 ].

For the purposes of our analysis, precipitation was included as a continuous variable monthly sum in millimeters , and lagged by two weeks, consistent with the lag used with the SP data. To assess potential effects of rainfall anomalies [ 75 ] during the period, we calculated the average monthly rainfall by bairro over the five years of study and computed the deviation of each bairro -month from its five year average. This variable was included in all models, except those stratified by monthly precipitation volume.

We defined the outcome variable as the monthly case incidence rate dengue cases per , people by bairro , observed from January to December , and used a zero-inflated negative binomial longitudinal regression model hereafter, longitudinal model. The zero-inflated negative binomial model accounts for extra-variation overdispersion in the data [ 78 , 79 ], and was considered as an alternative to the negative binomial hurdle model given the likelihood that seasonally inflated zeros are attributable to both true changes in ecological dynamics that depress transmission, as well as reduced surveillance or misclassification during low seasons and interepidemic years [ 80 — 82 ].

The final model was selected based on comparison of Akaike Information Criterion AIC values for alternative distributions, including negative binomial, poisson, and zero-inflated poisson. We chose longitudinal models because our data are longitudinal at the bairro level, and used Huber-White standard errors [ 83 ].

Considering the cyclical and seasonal patterns of dengue transmission, we run eight temporally stratified models. Model 1 contains all 7, bairro -months for which data were collected. Models 2 and 3 distinguish between epidemic , , and and interepidemic , years. Models 4 and 5 stratify the analysis by transmission intensity, while Models 6, 7, and 8 are stratified by the intensity of precipitation Fig 3.

We addressed the family-wise error rate in all models using the false discovery rate FDR [ 84 ]. All data preparation and regression analysis was done in Stata v. Months with high intensity of rainfall more than millimeters , medium mm , and low less than 15mm are represented in shades of blue. Lastly, to characterize the spatial and temporal patterns of epidemic intensity between years we used average linkage hierarchical clustering with an Euclidean distance dissimilarity measure, based on eight metrics, standardized as z-scores, reflecting the duration and burden of dengue incidence in a bairro.

The number of clusters selected for each year was determined with reference to the gap statistic [ 85 ], reflecting the difference of within-cluster variability as the within-cluster sum of squares around cluster means at each number of k clusters in the observed data from its expectation in a null reference distribution computed from repeated sampling using the package factoextra [ 86 ]. Values of the gap statistic indicate the strength of clustering at each quantity of clusters considered i.

Maxima of the gap statistic may be local in comparison to their immediate neighboring values k or global in comparison to all possible or considered levels k , and a plateau in the statistic indicates the negligible added value of additional partitions between clustering groups [ 85 ].

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We considered between 3—7 clusters for each year to preserve interpretative value, prioritizing global maxima of the gap statistic for years , , — S1 Fig , or local maxima that distinguish between well-separated groups, where further divisions between clusters in the considered range would exclusively partition single- bairro clusters Though the gap statistic first plateaued at three clusters in , we partitioned the middle cluster—with high within-cluster variability along the first principal component of our clustering parameters—to distinguish two clusters, each with low internal variability along that axis see S2 Fig , clusters II and III.

In addition to plots of the first two principal components of clustering parameters S2 Fig , grouping decisions considered potential informational value to surveillance operations. After grouping, clusters were ordered by the average full-year case rate of clustered units. Clustering analysis and mapping were conducted with R version 3. Data used in this study are available in S1 Dataset. Between and , , dengue cases were reported in Fortaleza. Annual citywide case rates per , varied from a low of in , to a high of 1, in Monthly rates within a single bairro exceeded 3, cases per , people in January , April , and June In total However, the pattern differed between epidemic and interepidemic years, as shown by the percent of annual reported cases that occurred between July and December each year Table 1 and Fig 4.

Incidence during epidemic years was higher, but more concentrated seasonally, while in interepidemic years transmission continued into seasons that were less climatologically hospitable to mosquitoes. Average precipitation by week is presented in blue. Line of weekly dengue case count citywide is presented in black. Scale is adjusted for dengue cases left y-axis between interepidemic , and epidemic years , , , and constant for precipitation right y-axis.

Hierarchical cluster analysis grouped bairros into ascending patterns according to the scale and duration of their dengue burden during annual transmission cycles Fig 5 , S2 Table. Spatially, results suggested that the epidemics of and were distinguishable by the increased prevalence of cases in northern, coastal bairros— such as the northern coast and northeastern peninsula near Mucuripe—after which incidence concentrated farther from the coastal urban core.

Small, outlying clusters typically composed of only one or two bairros with elevated incidence rates and shorter periods of transmission were present in all years, such that the distance between observations in patterns 1 and 2 was smaller than between other clustering levels, and clustering was strongest during years when a large majority of bairros reported minimal dengue transmission such as and S2 Fig and S3 Table. Averages and ranges of parameter values by year and pattern are presented in S2 Table. Patterns were ordered low-high by annual case rates.

Highest level patterns, particularly during lower-transmission years patterns 6 and 7 in and ; pattern 4 in , classify bairros with outlying case rates but shorter transmission intervals. Middle ranked patterns patterns 2 and 3 in ; pattern 3 in ; pattern 5 in and were characterized by more populous bairros with longer intervals with continuous transmission, typically exceeding 40 weeks annually, and higher total case counts. In , clustering isolated 15 bairros patterns 2—5 , spatially dispersed throughout the city, with substantially elevated or protracted transmission Fig 5 ; five of these bairros patterns 4 and 5 exhibited outlying incidence rates greater than 6, per , over shorter intervals S2 Table.

During the epidemic, transmission was present or elevated in all regionais : two bairros in the lowest clustering level experienced more than 3, cases per ,, over 44 and 48 weeks with confirmed incidence. The epidemic and interepidemic years featured lower levels of transmission overall and fewer outlying bairros , decreasing clustering distances between high and low patterns. As a result, clustering distinguished additional patterns at both high and low scales of transmission. Similar to the epidemic, transmission during the epidemic was spatially diffuse, with coastal, affluent bairros grouped amongst peripheral communities in pattern 2.

In contrast, in northeastern coastal bairros were almost exclusively grouped in pattern 1 Fig 5. This trend continued in subsequent years, when outlying transmission was either largely isolated or concentrated in bairros of the southern periphery. Confirmed incidence was negligible in bairros of pattern 1 in , yet low level residual dengue circulation is evident in six bairros of patterns 2 and 3 Messejana, Jangurussu, Barroso, Maraponga, Bom Jardim, and Mondubim , where cases were confirmed for The scale of annual transmission in these six bairros is comparable to pattern 1 bairros in , , and , but occurred over much longer intervals S2 Table.

Confirmed dengue incidence in three of these bairros Messejana, Jangurussu, and Barroso increased to epidemic levels in identifiable in patterns 4 and 5 , inflating case rates for the city as a whole. Table 2 presents descriptive statistics. Mean household size in Fortaleza was 3. Access to infrastructural and municipal services was high except for sewage , though unequally distributed.

Population density exceeded persons per km 2 in all bairros ranging from in Sabiaguaba to more than 34, persons per km 2 in Pirambu. Homicide rates by bairro ranged from zero to Temperature in Fortaleza did not vary substantially over the course of our five year study period, but daily temperature averages were marginally higher earlier in the year; the lowest daily temperature average was In contrast, precipitation differed seasonally, annually, and between bairros. The largest bairro differential in weekly precipitation was Average deviation from monthly averages, measured at the bairro level, was highest in February , when average bairro precipitation exceeded five-year averages for that month by nearly mm.

This trend continued for the following three months with positive deviations exceeding mm in March, April, and May , amidst the epidemic. Values of daily high, low, and average daily temperatures were obtained from each year. High values are represented by dots colored in shades of red, and low values by dots colored in shades of blue. The black line summarizes the daily average for the 5-year period, and the green line shows the standard deviation of daily averages by month. Entomological surveillance data is summarized in Table 3.

On average, bairro -level infestation was higher during the LIA survey than for LIRAa surveys , , and , though the maximum bairro -level infestation value 6. The highest average January bairro infestation over the course of the four-years excluding was in Cambeba, 3. The spatial distribution of SP types varied across the city Fig 7 shows their location in , and around 3, SP sites were inspected annually, on average, during the study period.

The largest number of SP inspected by surveillance agents was 3, in , the majority being tire repair shops and garages Tires category in Table 3. Considering all types of SPs, the highest infestation indices were registered in 8. The monthly areal proportion of a bairro within meters of a positive SP varied by type of site, reflecting differences in SP prevalence and infestation rates by class. Indicator variables of epidemic year, peak transmission season, and of precipitation intensity were statistically significant, supporting stratified analyses.

Tables 5 and 6 show the models stratified by transmission intensity inter-annually Models 2 and 3 and seasonally Models 4 and 5. Results indicate that some covariates were strongly associated with dengue incidence rates during periods of low Models 3 and 5 , but not high Models 2 and 4 transmission.

Further, while exhibiting a negative, protective effect in all strata, the percentage of properties with regular garbage collection was only statistically significant during interepidemic years Model 3. Conversely, though unit increases in exposure to construction, recycling, scrapyard, and tire sites were all positively correlated with dengue rates during interepidemic years, none were statistically significant when corrected for multiple testing Model 3. Unlike every other model, the continuous variable for monthly sum precipitation mm was not positively associated with incidence over the course of interepidemic years in our sample, reflecting the absence of typical dengue seasonality during those years.

Models 4 and 5 Table 6 present results stratified by monthly transmission intensity Fig 3. Though homicides were associated with dengue rates in all models, an additional 10 homicides per , was associated with a 7. Income was a statistically significant protective correlate during both high and low transmission months Models 4 and 5 , and—though not statistically significant when corrected for multiple testing—models estimated stronger associations between regular garbage collection and dengue incidence during late-season months of residual transmission Model 5. Finally, literacy was a statistically significant bairro -level correlate of dengue incidence rates during low months Model 5 , but with divergent effects: male literacy was associated with increased dengue incidence rates, while female literacy was correlated with lower rates.

The strongest associations between proximity to infested SPs and dengue incidence related to scrapyards and tire sites within both seasonal strata Models 4 and 5 ; while the estimated effects were larger during residual months Model 5 , the corrected associations were not statistically significant. Temperatures were slightly lower and more variable, on average, during epidemic years Table 2 ; nevertheless, a negative correlation between variability and incidence during months of seasonal transmission Model 4 suggests lower temperature variance during peak months of epidemic years relative to interepidemic years.

Models 6, 7, and 8 are stratified by precipitation intensity and presented in Table 7. Consistent with results for low-transmission months Model 5 , literacy and garbage collection were correlates of dengue incidence only during months with minimal rainfall Model 6. The estimated effect for these SP types declined to 2. While temperature variability was a strong predictor of monthly dengue incidence rates during periods with minimal precipitation Model 6 , it registered a null association during wetter months Model 8. This study analyzed five years of dengue incidence at fine spatial and temporal scales.

One of the most important findings was the distinct seasonal pattern between interepidemic and epidemic years. Our results indicate that dengue epidemiological curves differed according to the intensity of annual transmission: while the pattern of transmission during epidemic years conforms to widely documented seasonality, during non-epidemic years low-level transmission persisted into climatologically inhospitable conditions. Sustained transmission after June could result from relaxed control activity following a high season with minimal transmission.

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Such lapses during interepidemic periods are likely to compound entomological and epidemiological challenges for the next annual cycle, and call for sustained vector control activities regardless of transmission intensity. Bairros in the southern periphery of the city experienced a larger dengue burden than the coastal city center, particularly during interepidemic years, suggesting that sustained, non-seasonal transmission is linked to conditions in those peripheral areas.

Socio-ecological indicators of poverty and deprivation were correlated with higher bairro -level dengue incidence rates during seasonal and non-seasonal months—highest during non-epidemic years—but not across epidemics years. The most pronounced associations between Ae. In contrast to the SP class covariates which quantify the proportion of a bairro spatially proximate to an infested site in bi-monthly intervals , measures of the SP infestation index and entomological cross-sectional surveys LIA and LIRAa —ostensibly conducted to identify areas with heightened vulnerability to dengue transmission—did not reliably capture the variability in risk between seasons or bairros , corroborating other studies [ 88 , 89 ].

These results are not surprising, given that the relationship between larval indices and adult densities is diminished by adult flight [ 90 ], and variable survival rates of immature forms and productivity by container type [ 91 ]. These findings have direct programmatic implications, identifing places and stages of the epidemic cycle when targeted community interventions can be prioritized by the Fortaleza Municipal Health Secretariat.

While the contribution of non-residential sites such as the SPs to vector propagation has been discussed [ 92 ], minimal attention is given to their epidemiological relevance. In some cases, non-residential surveillance has been limited to natural sites and niches uncharacteristic of Ae. Consideration of non-residential structures and spaces that sustain oviposition during dry seasons—such as vacant lots [ 96 ], septic tanks [ 97 ], and drains [ 98 , 99 ]—is sparse. Although special surveillance of SPs is mandatory in Brazil, the absence of rigorous examination of the importance of SP infestation for dengue virus transmission limits the capacity of municipal actors to take evidence-based steps to improve their routine control activities.

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To the best of our knowledge, this is the first study to use fine scale information on SP inspection. Infestation of SPs was spatially and temporally associated with dengue incidence in Fortaleza, and the magnitude of the associations differed by SP type and according to the scale of transmission and precipitation.

Specifically, scrapyards and sites associated with tire collection and storage—such as repair shops and garages—showed associations with dengue incidence during periods of both interepidemic and epidemic transmission. The proportion of a bairro proximate to sites known for storing tires was a strong and reliable correlate of dengue incidence within nearly all strata, but the magnitude of the coefficient was largest during transitional precipitation regimes when rainfall was neither sparse nor extreme and dry seasons.

Further, after adjustment for FDR, scrapyards registered statistically significant correlations with dengue incidence rates during the same intermediate rainfall strata. These results, combined with the new findings regarding the seasonal pattern of dengue transmission in interepidemic years, strongly suggest that enhanced surveillance should be sustained during low transmission periods, in both epidemic and interepidemic years.

An enhanced program would also impose strict and transparent consequences for sustained infestation at these locations such as revocation of licenses or more effective fine enforcement , and where operators exhibit disregard for vector control protocol during successive visits. Further, many of the socioeconomic, structural, and environmental factors expected to be associated with dengue transmission showed varied significance according to transmission and precipitation intensity.

Among the factors related to access to public services, regular garbage collection was the most consistent correlate of dengue incidence, consistent with previous studies [ , ]. However, we also show that the link is most pronounced during low transmission and low precipitation periods.

Empty lots filled with abandoned trash—as well as discarded materials such as cans, plastic bottles, and debris commonly observed in yards of houses and along sidewalks [ 15 ]—could preserve desiccation-resistant eggs, which hatch following intermittent late-season rains.

Thus, closer property surveillance and scrutiny of empty lots may be an important component of targeted vector control during interepidemic periods to prevent the escalation to epidemic-scale transmission.


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With regard to income, negative [ ] and null [ ] associations between poverty and dengue incidence have been reported for Fortaleza. In contrast, with the exception of the epidemic year Model 2 and high precipitation Model 8 models, when incidence was shown to be more widely dispersed throughout the city, our results indicate a persistent concentration of dengue cases in lower income bairros. Nonetheless, these results need to be interpreted with caution: health care provided by the private sector is largely underreported in SINAN, despite the fact that notification is mandatory [ ].

Consistent and large correlations between dengue incidence and interpersonal violence, which was also observed for tuberculosis [ ], exemplify the difficulty of implementing effective population health measures in expansive cities such as Fortaleza, as well as the importance of involving different governmental sectors to address these challenges.

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In contrast to many other exposures, homicides were more strongly associated with dengue rates during epidemics and high transmission months. When violence deters actors tasked with providing municipal services it limits access to health services and implementation of responsive vector control [ ]. Vector control agents—who are often tasked with canvassing bairros and entering properties—may neglect areas that are rife with violence, replicating challenges to effective control of yellow fever that were encountered by Oswaldo Cruz in the early 20 th century [ ].

In conjunction, wary property owners may refuse entry to vector control and health service officers out of fear for their personal safety [ , ]. In the context of Aedes control, while achieving total coverage is rare, the larger the areas of the city that remain uninspected and thus untreated , the higher the threat to effective vector control [ ].

With regard to bairro structural factors, subnormal settlements were not associated with dengue transmission. This may reflect data limitations. Classification of a census tract as AS does not characterize the degree of subnormality, such as the proportion of houses that are subnormal, or the nature of the AS that predominates in a tract. Meireles or not e. Though we did not analyze serological data, the Fortaleza Municipal Health Secretariat conducts limited sampling to monitor the introduction of allochthonous serotypes.

DENV1 was reintroduced in after 10 years, and was the primary serotype in , and DENV4 was first introduced in , and was responsible for the majority of cases in and As a result of diminished population susceptibility, it is unlikely that DENV1 which caused an epidemic in was solely responsible for the estimated disease burden in Though imported cases of chikungunya CHIKV were detected by the surveillance of the City of Fortaleza in , autochthonous cases were not confirmed until December Therefore, the circulation of ZIKV in offers one possible explanation for the epidemiological curve observed for that year.

Related Biggest casualty of Nipah virus could be Kerala tourism. By evening, critical care specialist Anoop Kumar and neurologist C Jayakrishnan had begun to suspect Nipah virus infection. Samples were sent to the Manipal Centre for Virus Research in Bengaluru, with which Kerala government has a collaboration. By May 19, Nipah virus infection was confirmed. The Kerala government then responded quickly, and within days traced and isolated everybody who had come into contact with the infected people. But it does not mean that the virus is contained.

Kerala has welltrained doctors and epidemiologists. The Manipal Centre for Virus Research, which was involved in the detection of the Nipah virus, has set up two infectious diseases laboratories in Kerala in collaboration with the state government. It is this groundwork that has enabled the state to catch the infection early. Over the last decade, the Indian government has strengthened the surveillance and response system for catching disease outbreaks early.

And yet observers feel that the country has also been lucky with the location of the current outbreak. What if it had occurred at a place with minimum preparedness and medical facilities? In Siliguri in , 45 people died within weeks of the outbreak, 30 of them health workers or casual visitors to the hospitals. In , people died in Bangladesh before the disease was contained. In both cases, the cause of deaths was established in retrospective studies, long after the infection had subsided. Expertise on the Nipah virus was not established.

The Kerala outbreak has had a completely different response. It may have already been stifled in Kerala, with the isolation of all those who came into contact with patients. And yet, there were loopholes. Except one family that first caught infection, all other cases happened in hospital.

Virologists say that such infections and deaths could have been avoided if standard international practices were followed. The Nipah virus is not the only disease that is being closely watched by Indian epidemiologists at the moment. This disease, spread by ticks living on monkeys, was discovered in but had remained restricted to pockets of Karnataka till recently. In the last few years, it has spread to other states, causing deaths. In Goa, 35 people have been tested positive for the disease this year, with three deaths. No one knows why the Nipah virus made its sudden appearance in Kerala.

The disease is spread by bats, and people suspect that the destruction of bat habitats may have forced them from forests to human settlements. A similar cause is suspected in monkey fever as well, as habitat destruction has forced monkeys to move from forests. Crimean-Congo haemorrhagic fever, another disease transmitted by ticks, is also spreading in the country.

Dengue fever and chikungunya are spreading fast, and can cause major outbreaks in the future.

The Zika virus has arrived in India, and its future course is not known. Many Indian epidemiologists are keeping a close watch on Ebola, not yet present in the country, and some scientists have already begun thinking about work on a vaccine.

Epidemic: The Past, Present and Future of the Diseases that Made Us

Healthcare professionals say that the Nipah virus outbreak showed both the value of preparedness and the dangers of not following standard protocols. In , the institution formally set up the Manipal Centre for Virology Research.


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Manipal Centre for Virology set up a lab in Shimoga in Karnataka, and later in 10 other states. Kerala had two labs as part of this programme. In August last year, it prepared a training programme for dealing with Nipah virus outbreaks. India may have improved its disease surveillance network, but reducing and limiting disease outbreaks requires s e ve r a l o t h e r measures, many lying outside the scope of the healthcare system. Can India stop the destruction of forests and restore the natural habitats of wild animals?

Can the healthcare system practise the concept of One Health, where human and animal health are tackled together? Because adults have stronger immune systems than children and the elderly, researchers believe that their stronger responses to the flu may have proved deadly. In the decades since the Spanish flu, researchers have developed various immunomodulatory therapies that can help mitigate cytokine storms. But those treatments are hardly perfect, and nor are they widely available.

This means that, as in , we would likely see a huge loss of life among young adults and the middle-aged. And because life expectancy today is decades higher than it was a century ago, their deaths would be even more detrimental to the economy and to society, Chowell says. Amidst all the bad news, though, there is one chance for salvation: a universal influenza vaccine. Significant resources are finally being allocated to this longtime pipe dream, and efforts to develop such a breakthrough vaccine are gaining momentum.

But we can only wait and see whether it will arrive in time to prevent the next pandemic. Future Menu. What is BBC Future? Follow the Food. Future Now. Spanish flu: years on Disease What if a deadly influenza pandemic broke out today? Share on Facebook. Share on Twitter. Share on Reddit. Share on WhatsApp. Share by Email.